In [1]:
%matplotlib inline
In [2]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly.graph_objects as go
import plotly.express as px
from mpl_toolkits.basemap import Basemap
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from scipy.stats import ttest_ind
In [3]:
pd.set_option('display.max_rows', 200)
pd.set_option('display.max_columns', 200)

Football Players' Price Prediction. Analysing Some of Their Features.

We will analyse Fifa dataset for two years 2016 and 2020. It is obtained from Kaggle. There are several notebooks using this dataset in Kaggle. Good examples are: Notebook 1, Notebook 2 and Notebook 3. The 2016 data will be used for preliminary analysis and training a linear regression model. Then, we will try to predict players' value in 2020. This will not be a time series analysis, but rather an attempt to tell price based on changed characteristics of the players like age, club, abilities which can improve or deteriorate (in case of injury for example). The structure of the work is as following:

  1. Description of the dataset
  2. Importing and cleaning the data
  3. Perform EDA
  4. Create hypothesis and linear regression model and
  5. Check hypothesis and test model with new data

Description of the dataset

Most of the column names are self explanatory. I will mention only those which are not understandable for the general public. To start with - the group of: 'team_positions','ls', 'st', 'rs', 'lw', 'lf', 'cf', 'rf', 'rw', 'lam', 'cam', 'ram', 'lm', 'lcm', 'cm', 'rcm', 'rm', 'lwb', 'ldm', 'cdm', 'rdm', 'rwb', 'lb', 'lcb', 'cb', 'rcb', 'rb'.

These are abbreviations of players' positions on the field. Best they can be understood is by looking at the following picture.

playground

Import and Clean the data

In [4]:
players_16=pd.read_csv("data/players_16.csv",index_col="sofifa_id")
In [5]:
players_16.head()
Out[5]:
player_url short_name long_name age dob height_cm weight_kg nationality club overall potential value_eur wage_eur player_positions preferred_foot international_reputation weak_foot skill_moves work_rate body_type real_face release_clause_eur player_tags team_position team_jersey_number loaned_from joined contract_valid_until nation_position nation_jersey_number pace shooting passing dribbling defending physic gk_diving gk_handling gk_kicking gk_reflexes gk_speed gk_positioning player_traits attacking_crossing attacking_finishing attacking_heading_accuracy attacking_short_passing attacking_volleys skill_dribbling skill_curve skill_fk_accuracy skill_long_passing skill_ball_control movement_acceleration movement_sprint_speed movement_agility movement_reactions movement_balance power_shot_power power_jumping power_stamina power_strength power_long_shots mentality_aggression mentality_interceptions mentality_positioning mentality_vision mentality_penalties mentality_composure defending_marking defending_standing_tackle defending_sliding_tackle goalkeeping_diving goalkeeping_handling goalkeeping_kicking goalkeeping_positioning goalkeeping_reflexes ls st rs lw lf cf rf rw lam cam ram lm lcm cm rcm rm lwb ldm cdm rdm rwb lb lcb cb rcb rb
sofifa_id
158023 https://sofifa.com/player/158023/lionel-messi/... L. Messi Lionel Andrés Messi Cuccittini 28 1987-06-24 170 72 Argentina FC Barcelona 94 95 111000000 550000 RW, CF Left 5 4 4 Medium/Low Messi Yes NaN #Speedster, #Dribbler, #FK Specialist, #Acroba... RW 10.0 NaN 2004-07-01 2018.0 RW 10.0 92.0 88.0 86.0 95.0 24.0 62.0 NaN NaN NaN NaN NaN NaN Finesse Shot, Speed Dribbler (CPU AI Only), On... 80-4 93-1 71 88-1 85 96 89 90 79+3 96 95-1 90 92-2 92-2 95 80 68-5 76-1 59-1 88 48 22 90-2 90 74 NaN 13-12 23+2 21+1 6 11 15 14 8 87+3 87+3 87+3 91+3 91+3 91+3 91+3 91+3 91+3 91+3 91+3 90+3 82+3 82+3 82+3 90+3 62+3 57+3 57+3 57+3 62+3 57+3 44+3 44+3 44+3 57+3
20801 https://sofifa.com/player/20801/c-ronaldo-dos-... Cristiano Ronaldo Cristiano Ronaldo dos Santos Aveiro 30 1985-02-05 185 80 Portugal Real Madrid 93 93 85500000 475000 LW, LM Right 5 4 5 High/Low C. Ronaldo Yes NaN #Speedster, #Dribbler, #Distance Shooter, #Acr... LM 7.0 NaN 2009-07-01 2018.0 ST 7.0 92.0 93.0 80.0 91.0 33.0 78.0 NaN NaN NaN NaN NaN NaN Power Free-Kick, Flair, Long Shot Taker (CPU A... 82-1 95 86 81-1 87 93 88 77-2 72 91-1 91 93-1 90-3 92+2 62-1 94 94 87-2 79 93 62-1 29+5 93+2 81 85 NaN 22 31 23 7 11 15 14 11 91+3 91+3 91+3 90+3 91+3 91+3 91+3 90+3 88+3 88+3 88+3 88+3 80+3 80+3 80+3 88+3 64+3 60+3 60+3 60+3 64+3 60+3 52+3 52+3 52+3 60+3
9014 https://sofifa.com/player/9014/arjen-robben/16... A. Robben Arjen Robben 31 1984-01-23 180 80 Netherlands FC Bayern München 90 90 56000000 250000 RM, LM, RW Left 5 2 4 High/Low Normal Yes NaN #Speedster, #Dribbler, #Distance Shooter, #Acr... SUB 10.0 NaN 2009-08-28 2017.0 RW 11.0 92.0 86.0 82.0 92.0 32.0 64.0 NaN NaN NaN NaN NaN NaN Diver, Injury Prone, Avoids Using Weaker Foot,... 80 85 51-1 85-1 86 93 86+1 83 74-2 90-2 92-1 92-1 91-2 91 91 86 61 76-2 65 90 47 39 89 84 80 NaN 29 26 26 10 8 11 5 15 84+3 84+3 84+3 89+3 88+3 88+3 88+3 89+3 88+3 88+3 88+3 87+3 80+3 80+3 80+3 87+3 65+3 60+3 60+3 60+3 65+3 59+3 47+3 47+3 47+3 59+3
167495 https://sofifa.com/player/167495/manuel-neuer/... M. Neuer Manuel Neuer 29 1986-03-27 193 92 Germany FC Bayern München 90 90 58000000 250000 GK Right 5 4 1 Medium/Medium Normal Yes NaN NaN GK 1.0 NaN 2011-07-01 2019.0 GK 1.0 NaN NaN NaN NaN NaN NaN 85.0 87.0 91.0 86.0 60.0 90.0 GK Long Throw, 1-on-1 Rush 15-10 13-12 25 48+6 11-14 16-9 14-11 11-14 47+6 31 58 61 43 88 35 25-17 78 44 83 16-9 29 30 12-13 70+20 37 NaN 10-15 10-15 11-14 85-3 87+2 91 90 86 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
176580 https://sofifa.com/player/176580/luis-suarez/1... L. Suárez Luis Alberto Suárez Díaz 28 1987-01-24 182 85 Uruguay FC Barcelona 90 90 69000000 300000 ST Right 5 4 4 High/Medium Normal Yes NaN #Acrobat, #Clinical Finisher ST 9.0 NaN 2014-07-11 2019.0 NaN NaN 83.0 88.0 79.0 87.0 42.0 79.0 NaN NaN NaN NaN NaN NaN Diver, Beat Offside Trap, Flair, Technical Dri... 77 90-1 77+2 82 87+2 88-2 86 84 64 91+2 88 78-1 86 91 60 88+4 69 86 76 85+3 78 41 91+3 84 85 NaN 30 45 38 27 25 31 33 37 87+3 87+3 87+3 87+3 88+3 88+3 88+3 87+3 86+3 86+3 86+3 85+3 79+3 79+3 79+3 85+3 67+3 65+3 65+3 65+3 67+3 64+3 58+3 58+3 58+3 64+3
In [6]:
players_16.shape
Out[6]:
(14881, 103)

From the database we can see that some of the columns will not be necessary for the analysis. They are: "player_url","long_name" (we have "short_name"), "wage_eur". Usually, wage is directly related to value, so either we know both or try to guess them. Makes no sense to make prediction for one based on the knowledge of the other. For the same reason, "Release clause" should also be removed. It is determined together with the value. Anyway, in this dataset there is no info about it.

Let's now see some general information about the dataset.

In [7]:
players_16.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 14881 entries, 158023 to 11728
Columns: 103 entries, player_url to rb
dtypes: float64(17), int64(10), object(76)
memory usage: 11.8+ MB
In [8]:
players_16.isnull().sum().sort_values(ascending=False)
Out[8]:
mentality_composure           14881
release_clause_eur            14881
nation_jersey_number          13871
nation_position               13871
loaned_from                   13827
player_tags                   13625
gk_diving                     13263
gk_handling                   13263
gk_kicking                    13263
gk_reflexes                   13263
gk_speed                      13263
gk_positioning                13263
player_traits                  8119
rcb                            1618
ls                             1618
dribbling                      1618
shooting                       1618
pace                           1618
rs                             1618
passing                        1618
physic                         1618
st                             1618
rb                             1618
lw                             1618
rm                             1618
lam                            1618
cam                            1618
ram                            1618
lm                             1618
lcm                            1618
cm                             1618
rcm                            1618
lwb                            1618
lf                             1618
ldm                            1618
cdm                            1618
rdm                            1618
rwb                            1618
lb                             1618
lcb                            1618
cb                             1618
rw                             1618
defending                      1618
rf                             1618
cf                             1618
joined                         1225
team_position                   171
team_jersey_number              171
contract_valid_until            171
skill_moves                       0
short_name                        0
long_name                         0
age                               0
dob                               0
height_cm                         0
weight_kg                         0
nationality                       0
club                              0
potential                         0
overall                           0
work_rate                         0
value_eur                         0
wage_eur                          0
player_positions                  0
preferred_foot                    0
international_reputation          0
weak_foot                         0
body_type                         0
real_face                         0
skill_long_passing                0
attacking_crossing                0
power_strength                    0
goalkeeping_reflexes              0
goalkeeping_positioning           0
goalkeeping_kicking               0
goalkeeping_handling              0
goalkeeping_diving                0
defending_sliding_tackle          0
defending_standing_tackle         0
defending_marking                 0
mentality_penalties               0
mentality_vision                  0
mentality_positioning             0
mentality_interceptions           0
mentality_aggression              0
power_long_shots                  0
power_stamina                     0
attacking_finishing               0
power_jumping                     0
power_shot_power                  0
movement_balance                  0
movement_reactions                0
movement_agility                  0
movement_sprint_speed             0
movement_acceleration             0
skill_ball_control                0
skill_fk_accuracy                 0
skill_curve                       0
skill_dribbling                   0
attacking_volleys                 0
attacking_short_passing           0
attacking_heading_accuracy        0
player_url                        0
dtype: int64

There are some features which have Null values for quite a large amount of records. "mentality_composure", "nation_position", "nation_jersey_number", "loaned_from","player_tags" have 91.6-100% missing data, so they can be removed. "gk_kicking", "gk_reflexes", "gk_speed", "gk_positioning", "gk_diving", "gk_handling" also have many null values but that is because the features are related to goal-keepers which are naturally much less than the field players. So we will keep them for now.

In [9]:
#Remove unnecessary columns

players_16=players_16.drop(["player_url","long_name","wage_eur","release_clause_eur","mentality_composure", "nation_position", "nation_jersey_number", "loaned_from","player_tags"],axis=1)
In [10]:
players_16.head()
Out[10]:
short_name age dob height_cm weight_kg nationality club overall potential value_eur player_positions preferred_foot international_reputation weak_foot skill_moves work_rate body_type real_face team_position team_jersey_number joined contract_valid_until pace shooting passing dribbling defending physic gk_diving gk_handling gk_kicking gk_reflexes gk_speed gk_positioning player_traits attacking_crossing attacking_finishing attacking_heading_accuracy attacking_short_passing attacking_volleys skill_dribbling skill_curve skill_fk_accuracy skill_long_passing skill_ball_control movement_acceleration movement_sprint_speed movement_agility movement_reactions movement_balance power_shot_power power_jumping power_stamina power_strength power_long_shots mentality_aggression mentality_interceptions mentality_positioning mentality_vision mentality_penalties defending_marking defending_standing_tackle defending_sliding_tackle goalkeeping_diving goalkeeping_handling goalkeeping_kicking goalkeeping_positioning goalkeeping_reflexes ls st rs lw lf cf rf rw lam cam ram lm lcm cm rcm rm lwb ldm cdm rdm rwb lb lcb cb rcb rb
sofifa_id
158023 L. Messi 28 1987-06-24 170 72 Argentina FC Barcelona 94 95 111000000 RW, CF Left 5 4 4 Medium/Low Messi Yes RW 10.0 2004-07-01 2018.0 92.0 88.0 86.0 95.0 24.0 62.0 NaN NaN NaN NaN NaN NaN Finesse Shot, Speed Dribbler (CPU AI Only), On... 80-4 93-1 71 88-1 85 96 89 90 79+3 96 95-1 90 92-2 92-2 95 80 68-5 76-1 59-1 88 48 22 90-2 90 74 13-12 23+2 21+1 6 11 15 14 8 87+3 87+3 87+3 91+3 91+3 91+3 91+3 91+3 91+3 91+3 91+3 90+3 82+3 82+3 82+3 90+3 62+3 57+3 57+3 57+3 62+3 57+3 44+3 44+3 44+3 57+3
20801 Cristiano Ronaldo 30 1985-02-05 185 80 Portugal Real Madrid 93 93 85500000 LW, LM Right 5 4 5 High/Low C. Ronaldo Yes LM 7.0 2009-07-01 2018.0 92.0 93.0 80.0 91.0 33.0 78.0 NaN NaN NaN NaN NaN NaN Power Free-Kick, Flair, Long Shot Taker (CPU A... 82-1 95 86 81-1 87 93 88 77-2 72 91-1 91 93-1 90-3 92+2 62-1 94 94 87-2 79 93 62-1 29+5 93+2 81 85 22 31 23 7 11 15 14 11 91+3 91+3 91+3 90+3 91+3 91+3 91+3 90+3 88+3 88+3 88+3 88+3 80+3 80+3 80+3 88+3 64+3 60+3 60+3 60+3 64+3 60+3 52+3 52+3 52+3 60+3
9014 A. Robben 31 1984-01-23 180 80 Netherlands FC Bayern München 90 90 56000000 RM, LM, RW Left 5 2 4 High/Low Normal Yes SUB 10.0 2009-08-28 2017.0 92.0 86.0 82.0 92.0 32.0 64.0 NaN NaN NaN NaN NaN NaN Diver, Injury Prone, Avoids Using Weaker Foot,... 80 85 51-1 85-1 86 93 86+1 83 74-2 90-2 92-1 92-1 91-2 91 91 86 61 76-2 65 90 47 39 89 84 80 29 26 26 10 8 11 5 15 84+3 84+3 84+3 89+3 88+3 88+3 88+3 89+3 88+3 88+3 88+3 87+3 80+3 80+3 80+3 87+3 65+3 60+3 60+3 60+3 65+3 59+3 47+3 47+3 47+3 59+3
167495 M. Neuer 29 1986-03-27 193 92 Germany FC Bayern München 90 90 58000000 GK Right 5 4 1 Medium/Medium Normal Yes GK 1.0 2011-07-01 2019.0 NaN NaN NaN NaN NaN NaN 85.0 87.0 91.0 86.0 60.0 90.0 GK Long Throw, 1-on-1 Rush 15-10 13-12 25 48+6 11-14 16-9 14-11 11-14 47+6 31 58 61 43 88 35 25-17 78 44 83 16-9 29 30 12-13 70+20 37 10-15 10-15 11-14 85-3 87+2 91 90 86 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
176580 L. Suárez 28 1987-01-24 182 85 Uruguay FC Barcelona 90 90 69000000 ST Right 5 4 4 High/Medium Normal Yes ST 9.0 2014-07-11 2019.0 83.0 88.0 79.0 87.0 42.0 79.0 NaN NaN NaN NaN NaN NaN Diver, Beat Offside Trap, Flair, Technical Dri... 77 90-1 77+2 82 87+2 88-2 86 84 64 91+2 88 78-1 86 91 60 88+4 69 86 76 85+3 78 41 91+3 84 85 30 45 38 27 25 31 33 37 87+3 87+3 87+3 87+3 88+3 88+3 88+3 87+3 86+3 86+3 86+3 85+3 79+3 79+3 79+3 85+3 67+3 65+3 65+3 65+3 67+3 64+3 58+3 58+3 58+3 64+3
In [11]:
players_16.shape
Out[11]:
(14881, 94)
In [12]:
players_16.describe().transpose()
Out[12]:
count mean std min 25% 50% 75% max
age 14881.0 2.493045e+01 4.603521e+00 16.0 21.00 24.0 28.00 45.0
height_cm 14881.0 1.811025e+02 6.701927e+00 155.0 176.00 181.0 186.00 204.0
weight_kg 14881.0 7.542820e+01 6.933863e+00 50.0 70.00 75.0 80.00 110.0
overall 14881.0 6.565043e+01 7.095894e+00 44.0 61.00 66.0 70.00 94.0
potential 14881.0 7.028869e+01 6.293664e+00 44.0 66.00 70.0 75.00 95.0
value_eur 14881.0 1.766313e+06 4.172073e+06 0.0 250000.00 575000.0 1400000.00 111000000.0
international_reputation 14881.0 1.119145e+00 3.972144e-01 1.0 1.00 1.0 1.00 5.0
weak_foot 14881.0 2.942477e+00 6.580131e-01 1.0 3.00 3.0 3.00 5.0
skill_moves 14881.0 2.290572e+00 7.293560e-01 1.0 2.00 2.0 3.00 5.0
team_jersey_number 14710.0 1.942536e+01 1.619540e+01 1.0 8.00 17.0 26.00 99.0
contract_valid_until 14710.0 2.017491e+03 1.734312e+00 2015.0 2016.00 2017.0 2019.00 2021.0
pace 13263.0 6.843550e+01 1.102372e+01 21.0 62.00 69.0 76.00 96.0
shooting 13263.0 5.260288e+01 1.396492e+01 14.0 42.00 55.0 63.00 93.0
passing 13263.0 5.707110e+01 1.083942e+01 20.0 50.00 58.0 65.00 93.0
dribbling 13263.0 6.194745e+01 1.075474e+01 22.0 56.00 63.0 69.00 95.0
defending 13263.0 5.099578e+01 1.767604e+01 15.0 34.00 56.0 65.00 90.0
physic 13263.0 6.530287e+01 9.631893e+00 27.0 59.00 66.0 72.00 89.0
gk_diving 1618.0 6.635600e+01 7.691925e+00 36.0 61.00 66.0 71.00 88.0
gk_handling 1618.0 6.244438e+01 8.300535e+00 42.0 56.25 63.0 68.00 87.0
gk_kicking 1618.0 6.101978e+01 8.286959e+00 29.0 55.00 60.0 66.75 91.0
gk_reflexes 1618.0 6.768232e+01 8.193498e+00 32.0 62.00 67.0 73.00 90.0
gk_speed 1618.0 4.375464e+01 8.375229e+00 12.0 39.00 44.0 48.00 64.0
gk_positioning 1618.0 6.318665e+01 8.869550e+00 37.0 57.00 63.0 69.00 90.0

These are the statistics for the numerical features. One of them is considered a numeral value but in fact its meaning is categorical - team_jersey_number. This should be taken into account later, in the preparation for submitting the dataset to the ML algorithm. Besides, there are several attributes which are in fact numerical values but due to the way they are input in the table, they are considered non numerical. These are the following columns: 'attacking_crossing', 'attacking_finishing', 'attacking_heading_accuracy', 'attacking_short_passing', 'attacking_volleys', 'skill_dribbling', 'skill_curve', 'skill_fk_accuracy', 'skill_long_passing', 'skill_ball_control', 'movement_acceleration', 'movement_sprint_speed', 'movement_agility', 'movement_reactions', 'movement_balance', 'power_shot_power', 'power_jumping', 'power_stamina', 'power_strength', 'power_long_shots', 'mentality_aggression', 'mentality_interceptions', 'mentality_positioning', 'mentality_vision', 'mentality_penalties', 'defending_marking', 'defending_standing_tackle', 'defending_sliding_tackle', 'goalkeeping_diving', 'goalkeeping_handling', 'goalkeeping_kicking', 'goalkeeping_positioning', 'goalkeeping_reflexes', 'ls', 'st', 'rs', 'lw', 'lf', 'cf', 'rf', 'rw', 'lam', 'cam', 'ram', 'lm', 'lcm', 'cm', 'rcm', 'rm', 'lwb', 'ldm', 'cdm', 'rdm', 'rwb', 'lb', 'lcb', 'cb', 'rcb', 'rb'.

The values are written in the format "Number1+number2" (i.e. "78+1", "84+3"). Sample is shown below.

In [13]:
players_16[['attacking_crossing','attacking_finishing', 'attacking_heading_accuracy',
       'attacking_short_passing', 'attacking_volleys', 'skill_dribbling',
       'skill_curve', 'skill_fk_accuracy', 'skill_long_passing',
       'skill_ball_control', 'movement_acceleration', 'movement_sprint_speed',
       'movement_agility', 'movement_reactions', 'movement_balance',
       'power_shot_power', 'power_jumping', 'power_stamina', 'power_strength',
       'power_long_shots', 'mentality_aggression', 'mentality_interceptions',
       'mentality_positioning', 'mentality_vision', 'mentality_penalties',
       'defending_marking', 'defending_standing_tackle',
       'defending_sliding_tackle', 'goalkeeping_diving',
       'goalkeeping_handling', 'goalkeeping_kicking',
       'goalkeeping_positioning', 'goalkeeping_reflexes', 'ls', 'st', 'rs',
       'lw', 'lf', 'cf', 'rf', 'rw', 'lam', 'cam', 'ram', 'lm', 'lcm', 'cm',
       'rcm', 'rm', 'lwb', 'ldm', 'cdm', 'rdm', 'rwb', 'lb', 'lcb', 'cb',
       'rcb', 'rb']].sample(7)
Out[13]:
attacking_crossing attacking_finishing attacking_heading_accuracy attacking_short_passing attacking_volleys skill_dribbling skill_curve skill_fk_accuracy skill_long_passing skill_ball_control movement_acceleration movement_sprint_speed movement_agility movement_reactions movement_balance power_shot_power power_jumping power_stamina power_strength power_long_shots mentality_aggression mentality_interceptions mentality_positioning mentality_vision mentality_penalties defending_marking defending_standing_tackle defending_sliding_tackle goalkeeping_diving goalkeeping_handling goalkeeping_kicking goalkeeping_positioning goalkeeping_reflexes ls st rs lw lf cf rf rw lam cam ram lm lcm cm rcm rm lwb ldm cdm rdm rwb lb lcb cb rcb rb
sofifa_id
200197 49+1 74+1 74+1 67+1 75+1 70+1 55+1 54+1 63+1 73+1 66 64 67 73+1 63 71+1 73+4 70 67 71+1 47+1 41+1 76+1 43+1 72+1 33+1 30+1 27+1 13+1 10+1 15+1 15+1 15+1 72+0 72+0 72+0 67+0 69+0 69+0 69+0 67+0 67+0 67+0 67+0 66+0 63+0 63+0 63+0 66+0 53+0 53+0 53+0 53+0 53+0 51+0 49+0 49+0 49+0 51+0
158663 66+2 66-3 53+1 66-3 68+1 69-1 56+1 61+1 65+1 68-3 85 84 71+2 66-1 73+1 67+1 66 72 65-2 67-1 61+1 52-8 62 58 69+1 62 61-3 59 6+1 13+1 16+1 6+1 9+1 66+0 66+0 66+0 69+0 67+0 67+0 67+0 69+0 67+0 67+0 67+0 68+0 65+0 65+0 65+0 68+0 66+0 63+0 63+0 63+0 66+0 65+0 61+0 61+0 61+0 65+0
201417 64+1 45+1 61+1 65+1 29+1 65+1 47+1 38+1 64+1 66+1 68-3 72 62 64+1 64 53+1 80 70 74+1 35+1 70+1 64 52+1 58+1 48+1 67+4 69+3 69+4 15+1 8+1 12+1 11+1 9+1 57+0 57+0 57+0 61+0 59+0 59+0 59+0 61+0 60+0 60+0 60+0 63+0 62+0 62+0 62+0 63+0 67+0 66+0 66+0 66+0 67+0 67+0 68+0 68+0 68+0 67+0
205198 67+5 53+21 54+1 63+3 32+1 67+3 55+21 41+1 61+1 54+9 67-1 66-1 74+20 47-16 76 34+1 70+15 58 47 52+20 63+1 55+11 56+2 45+1 42+1 52+1 51+4 46+1 12+1 9+1 15+1 10+1 10+1 53+0 53+0 53+0 59+0 56+0 56+0 56+0 59+0 57+0 57+0 57+0 59+0 56+0 56+0 56+0 59+0 57+0 55+0 55+0 55+0 57+0 56+0 53+0 53+0 53+0 56+0
213423 60-3 57+1 32+1 62-1 61-2 59+1 49+1 42+1 53+1 62+1 82+9 83+5 68 49+1 71 58+1 53 76+8 75+10 55+1 50+1 50+1 53+1 49+1 50+1 59+1 62+1 51+1 13+1 7+1 13+1 12+1 9+1 58+0 58+0 58+0 61+0 59+0 59+0 59+0 61+0 59+0 59+0 59+0 61+0 57+0 57+0 57+0 61+0 61+0 58+0 58+0 58+0 61+0 60+0 56+0 56+0 56+0 60+0
219120 14 12 15 32 9 19 12 11 27 22 44 45 35 48 46 20 58 27 60 11 27 16 14 43 13 18 20 15 60 58 56 60 62 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
207513 53+1 52+1 47+1 63+1 51+1 60+1 43+1 58+1 58+1 58+1 65 68 57 54+1 74+1 55+1 75+4 59 49 54+1 60+1 54+1 53+1 51+1 55+1 51+1 54+1 56+1 15+1 14+1 6+1 8+1 15+1 55+0 55+0 55+0 57+0 57+0 57+0 57+0 57+0 57+0 57+0 57+0 58+0 57+0 57+0 57+0 58+0 57+0 56+0 56+0 56+0 57+0 57+0 55+0 55+0 55+0 57+0

The numbers added after the "+/-" sign come from international reputation of player. So, for analysis purposes they can be added/subtracted.

Before that, we have to handle the NaN values in several features. The ones that depict some abilitites of the players. Main reason for not having value is that either they are qualities of field players in the records for a goal keeper or vice versa. So, it is normal to replace them by zeroes.

One more thing is noticed while checking the data. There is a kind of duplication of data. For example, columns "gk_diving","gk_handling", "gk_kicking","gk_reflexes" and "gk_positioning" look quite similar to 'goalkeeping_diving','goalkeeping_handling', 'goalkeeping_kicking','goalkeeping_reflexes','goalkeeping_positioning'. The difference is that in the first group for the field players any rating about goalkeeping capabilities is missing, while in the second group there are some. We would better keep the second group because sometimes GK qualities are useful for field players.

In [14]:
players_16=players_16.drop(["gk_diving","gk_handling", "gk_kicking","gk_reflexes","gk_positioning"],axis=1)

Thus, the columns which need filling with zeroes for NaN values and then transforming this type of string "Number1+number2" into a mathematical calculation are the following ones, put in the list "cols_for_fill".

In [15]:
cols_for_fill=['pace',
       'shooting', 'passing', 'dribbling', 'defending', 'physic', 'gk_speed',
       'attacking_crossing',
       'attacking_finishing', 'attacking_heading_accuracy',
       'attacking_short_passing', 'attacking_volleys', 'skill_dribbling',
       'skill_curve', 'skill_fk_accuracy', 'skill_long_passing',
       'skill_ball_control', 'movement_acceleration', 'movement_sprint_speed',
       'movement_agility', 'movement_reactions', 'movement_balance',
       'power_shot_power', 'power_jumping', 'power_stamina', 'power_strength',
       'power_long_shots', 'mentality_aggression', 'mentality_interceptions',
       'mentality_positioning', 'mentality_vision', 'mentality_penalties',
       'defending_marking', 'defending_standing_tackle',
       'defending_sliding_tackle', 'goalkeeping_diving',
       'goalkeeping_handling', 'goalkeeping_kicking',
       'goalkeeping_positioning', 'goalkeeping_reflexes', 'ls', 'st', 'rs',
       'lw', 'lf', 'cf', 'rf', 'rw', 'lam', 'cam', 'ram', 'lm', 'lcm', 'cm',
       'rcm', 'rm', 'lwb', 'ldm', 'cdm', 'rdm', 'rwb', 'lb', 'lcb', 'cb',
       'rcb', 'rb']

The columns that need transformation are too many and it will be time consuming to do that manually. That's why we will create a function. It converts the values to string, substitutes "NaN" with "0" and then makes the string a mathematical expression (by using eval()).

In [16]:
def transform_columns_to_number(data_frame,columns_to_be_transformed):
    for col in columns_to_be_transformed:
        data_frame[col]=data_frame[col].astype(str)
        data_frame[col]=data_frame[col].replace("nan","0")
        data_frame[col]=pd.Series([eval(item) for item in data_frame[col].values]).values
    #return data_frame[col]    
In [17]:
transform_columns_to_number(players_16,cols_for_fill)
In [18]:
players_16.head()
Out[18]:
short_name age dob height_cm weight_kg nationality club overall potential value_eur player_positions preferred_foot international_reputation weak_foot skill_moves work_rate body_type real_face team_position team_jersey_number joined contract_valid_until pace shooting passing dribbling defending physic gk_speed player_traits attacking_crossing attacking_finishing attacking_heading_accuracy attacking_short_passing attacking_volleys skill_dribbling skill_curve skill_fk_accuracy skill_long_passing skill_ball_control movement_acceleration movement_sprint_speed movement_agility movement_reactions movement_balance power_shot_power power_jumping power_stamina power_strength power_long_shots mentality_aggression mentality_interceptions mentality_positioning mentality_vision mentality_penalties defending_marking defending_standing_tackle defending_sliding_tackle goalkeeping_diving goalkeeping_handling goalkeeping_kicking goalkeeping_positioning goalkeeping_reflexes ls st rs lw lf cf rf rw lam cam ram lm lcm cm rcm rm lwb ldm cdm rdm rwb lb lcb cb rcb rb
sofifa_id
158023 L. Messi 28 1987-06-24 170 72 Argentina FC Barcelona 94 95 111000000 RW, CF Left 5 4 4 Medium/Low Messi Yes RW 10.0 2004-07-01 2018.0 92.0 88.0 86.0 95.0 24.0 62.0 0.0 Finesse Shot, Speed Dribbler (CPU AI Only), On... 76 92 71 87 85 96 89 90 82 96 94 90 90 90 95 80 63 75 58 88 48 22 88 90 74 1 25 22 6 11 15 14 8 90 90 90 94 94 94 94 94 94 94 94 93 85 85 85 93 65 60 60 60 65 60 47 47 47 60
20801 Cristiano Ronaldo 30 1985-02-05 185 80 Portugal Real Madrid 93 93 85500000 LW, LM Right 5 4 5 High/Low C. Ronaldo Yes LM 7.0 2009-07-01 2018.0 92.0 93.0 80.0 91.0 33.0 78.0 0.0 Power Free-Kick, Flair, Long Shot Taker (CPU A... 81 95 86 80 87 93 88 75 72 90 91 92 87 94 61 94 94 85 79 93 61 34 95 81 85 22 31 23 7 11 15 14 11 94 94 94 93 94 94 94 93 91 91 91 91 83 83 83 91 67 63 63 63 67 63 55 55 55 63
9014 A. Robben 31 1984-01-23 180 80 Netherlands FC Bayern München 90 90 56000000 RM, LM, RW Left 5 2 4 High/Low Normal Yes SUB 10.0 2009-08-28 2017.0 92.0 86.0 82.0 92.0 32.0 64.0 0.0 Diver, Injury Prone, Avoids Using Weaker Foot,... 80 85 50 84 86 93 87 83 72 88 91 91 89 91 91 86 61 74 65 90 47 39 89 84 80 29 26 26 10 8 11 5 15 87 87 87 92 91 91 91 92 91 91 91 90 83 83 83 90 68 63 63 63 68 62 50 50 50 62
167495 M. Neuer 29 1986-03-27 193 92 Germany FC Bayern München 90 90 58000000 GK Right 5 4 1 Medium/Medium Normal Yes GK 1.0 2011-07-01 2019.0 0.0 0.0 0.0 0.0 0.0 0.0 60.0 GK Long Throw, 1-on-1 Rush 5 1 25 54 -3 7 3 -3 53 31 58 61 43 88 35 8 78 44 83 7 29 30 -1 90 37 -5 -5 -3 82 89 91 90 86 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
176580 L. Suárez 28 1987-01-24 182 85 Uruguay FC Barcelona 90 90 69000000 ST Right 5 4 4 High/Medium Normal Yes ST 9.0 2014-07-11 2019.0 83.0 88.0 79.0 87.0 42.0 79.0 0.0 Diver, Beat Offside Trap, Flair, Technical Dri... 77 89 79 82 89 86 86 84 64 93 88 77 86 91 60 92 69 86 76 88 78 41 94 84 85 30 45 38 27 25 31 33 37 90 90 90 90 91 91 91 90 89 89 89 88 82 82 82 88 70 68 68 68 70 67 61 61 61 67

Need to check for missing data.

In [19]:
players_16.isnull().sum().sort_values(ascending=False)
Out[19]:
player_traits                 8119
joined                        1225
contract_valid_until           171
team_jersey_number             171
team_position                  171
attacking_finishing              0
dribbling                        0
defending                        0
physic                           0
gk_speed                         0
attacking_crossing               0
attacking_heading_accuracy       0
shooting                         0
attacking_short_passing          0
attacking_volleys                0
skill_dribbling                  0
skill_curve                      0
skill_fk_accuracy                0
skill_long_passing               0
skill_ball_control               0
movement_acceleration            0
movement_sprint_speed            0
passing                          0
rb                               0
pace                             0
movement_reactions               0
age                              0
dob                              0
height_cm                        0
weight_kg                        0
nationality                      0
club                             0
overall                          0
potential                        0
value_eur                        0
player_positions                 0
preferred_foot                   0
international_reputation         0
weak_foot                        0
skill_moves                      0
work_rate                        0
body_type                        0
real_face                        0
movement_agility                 0
movement_balance                 0
rcb                              0
power_shot_power                 0
cf                               0
rf                               0
rw                               0
lam                              0
cam                              0
ram                              0
lm                               0
lcm                              0
cm                               0
rcm                              0
rm                               0
lwb                              0
ldm                              0
cdm                              0
rdm                              0
rwb                              0
lb                               0
lcb                              0
cb                               0
lf                               0
lw                               0
rs                               0
mentality_penalties              0
power_jumping                    0
power_stamina                    0
power_strength                   0
power_long_shots                 0
mentality_aggression             0
mentality_interceptions          0
mentality_positioning            0
mentality_vision                 0
defending_marking                0
st                               0
defending_standing_tackle        0
defending_sliding_tackle         0
goalkeeping_diving               0
goalkeeping_handling             0
goalkeeping_kicking              0
goalkeeping_positioning          0
goalkeeping_reflexes             0
ls                               0
short_name                       0
dtype: int64

"player_traits" for now we will leave as it is. Anyway, it will not be submitted to the ML algorithm. "joined","team_position" and "contract_valid_until" seems logical to substitute with the most common value. While "team_jersey_number" we would replace with "0". We again need a function which will do the work for us.

In [20]:
def fill_missing_data(dataframe,columns):
    for col in columns:
        dataframe[col]=dataframe[col].fillna(dataframe[col].mode()[0])
In [21]:
columns=["joined","team_position","contract_valid_until"]
fill_missing_data(players_16,columns)
In [22]:
players_16["team_jersey_number"]=players_16["team_jersey_number"].fillna(0)
In [23]:
players_16.isnull().sum().sort_values(ascending=False)
Out[23]:
player_traits                 8119
rb                               0
movement_reactions               0
shooting                         0
passing                          0
dribbling                        0
defending                        0
physic                           0
gk_speed                         0
attacking_crossing               0
attacking_finishing              0
attacking_heading_accuracy       0
attacking_short_passing          0
attacking_volleys                0
skill_dribbling                  0
skill_curve                      0
skill_fk_accuracy                0
skill_long_passing               0
skill_ball_control               0
movement_acceleration            0
movement_sprint_speed            0
pace                             0
contract_valid_until             0
joined                           0
value_eur                        0
age                              0
dob                              0
height_cm                        0
weight_kg                        0
nationality                      0
club                             0
overall                          0
potential                        0
player_positions                 0
team_jersey_number               0
preferred_foot                   0
international_reputation         0
weak_foot                        0
skill_moves                      0
work_rate                        0
body_type                        0
real_face                        0
team_position                    0
movement_agility                 0
movement_balance                 0
rcb                              0
power_shot_power                 0
cf                               0
rf                               0
rw                               0
lam                              0
cam                              0
ram                              0
lm                               0
lcm                              0
cm                               0
rcm                              0
rm                               0
lwb                              0
ldm                              0
cdm                              0
rdm                              0
rwb                              0
lb                               0
lcb                              0
cb                               0
lf                               0
lw                               0
rs                               0
mentality_penalties              0
power_jumping                    0
power_stamina                    0
power_strength                   0
power_long_shots                 0
mentality_aggression             0
mentality_interceptions          0
mentality_positioning            0
mentality_vision                 0
defending_marking                0
st                               0
defending_standing_tackle        0
defending_sliding_tackle         0
goalkeeping_diving               0
goalkeeping_handling             0
goalkeeping_kicking              0
goalkeeping_positioning          0
goalkeeping_reflexes             0
ls                               0
short_name                       0
dtype: int64

Now all columns are filled with values, except for "player_traites".

Let's do some EDA

The aim is to see some interesting statistics about players and teams related to nationality, clubs, prices, age, etc.

First we will group the players by nationality and see which are the top 50 nationalities with largest number of players.

There will be many plottings, so in order to make our lives easy, we'd better make a function with appropriate parameters.

In [24]:
def draw_graphs(dataset,title,analyzed_feature,groupby_feature=None, stat_function=None,asc=None,orient=None,
               xtitle=None,ytitle=None,num_recs=None,marker_color="blue"):
    """
    Parameters and values they can take:
    stat_function="max","sum,"mean","count"
    orient="v","h"
       
    
    """
    if stat_function=="count":
        graph_data=dataset.groupby(groupby_feature)[analyzed_feature].count().sort_values(ascending=asc)[0:num_recs]
    elif stat_function=="max":
        graph_data=dataset.groupby(groupby_feature)[analyzed_feature].max().sort_values(ascending=asc)[0:num_recs]
    elif stat_function=="sum":
        graph_data=dataset.groupby(groupby_feature)[analyzed_feature].sum().sort_values(ascending=asc)[0:num_recs]
    else:
        graph_data=dataset.groupby(groupby_feature)[analyzed_feature].mean().sort_values(ascending=asc)[0:num_recs]
    if orient=='v':
        fig=px.bar(graph_data,x=graph_data.index,y=graph_data,orientation=orient,width=900, height=500)
    else:
        fig=px.bar(graph_data,x=graph_data,y=graph_data.index,orientation=orient,width=900, height=500)
    fig.update_layout(
    title_text=title,
    xaxis_title_text=xtitle,
    yaxis_title_text=ytitle) 
    fig.update_traces(marker_color=marker_color) 
    fig.show()
    
In [25]:
draw_graphs(dataset=players_16,title="Number of players per Nation (top 50)",
            analyzed_feature="nationality",groupby_feature="nationality",
            asc=False,xtitle="Nation",ytitle="Total number of players",
            stat_function="count",
            orient='v',num_recs=50,marker_color="violet")

Let's explore the age of players. We will take the top 20 most represented countries. We will calculate the average age per country and see some distributions.

In [26]:
top_20_nations=players_16.groupby("nationality")["nationality"].count().sort_values(ascending=False)[:20]

# Selecting all players which nationalities are in the top 20 most represented countries.

players_top_20_nations=players_16[players_16["nationality"].isin(top_20_nations.index)]
In [27]:
draw_graphs(dataset=players_top_20_nations,title="Average age of player per nationality",
            analyzed_feature="age",groupby_feature="nationality",
            asc=True,xtitle="Age",ytitle="Nationality",
            stat_function="mean",
            orient='h',num_recs=20)

The difference between lowest and highest age is about 2.5 years. Which is kind of expected. It is worth mentioning that youngest players come from England and the Netherlands which might be a sign of productive schools for young footballers.

In [28]:
nationality_and_age=players_top_20_nations[["nationality","age"]]
fig = px.box(nationality_and_age, x="nationality", y="age")

fig.show()

It is interesting to see who the outliers are.

In [29]:
nations_list=["England","Sweden", "Colombia","Mexico","Norway"]
outliers=pd.DataFrame()
for nation in nations_list:
    player_nationality=nation
    nationality_max_age=players_top_20_nations[players_top_20_nations["nationality"]==nation]["age"].max()
    outliers=outliers.append(players_top_20_nations[(players_top_20_nations["nationality"]==nation) & (players_top_20_nations["age"]==nationality_max_age)])# & players_top_20_nations["age"]==nationality_max_age])
outliers
Out[29]:
short_name age dob height_cm weight_kg nationality club overall potential value_eur player_positions preferred_foot international_reputation weak_foot skill_moves work_rate body_type real_face team_position team_jersey_number joined contract_valid_until pace shooting passing dribbling defending physic gk_speed player_traits attacking_crossing attacking_finishing attacking_heading_accuracy attacking_short_passing attacking_volleys skill_dribbling skill_curve skill_fk_accuracy skill_long_passing skill_ball_control movement_acceleration movement_sprint_speed movement_agility movement_reactions movement_balance power_shot_power power_jumping power_stamina power_strength power_long_shots mentality_aggression mentality_interceptions mentality_positioning mentality_vision mentality_penalties defending_marking defending_standing_tackle defending_sliding_tackle goalkeeping_diving goalkeeping_handling goalkeeping_kicking goalkeeping_positioning goalkeeping_reflexes ls st rs lw lf cf rf rw lam cam ram lm lcm cm rcm rm lwb ldm cdm rdm rwb lb lcb cb rcb rb
sofifa_id
11728 B. Richardson 45 1969-08-05 185 77 England Wycombe Wanderers 44 44 10000 GK Right 1 2 1 Medium/Medium Stocky No SUB 13.0 2014-01-30 2021.0 0.0 0.0 0.0 0.0 0.0 0.0 25.0 NaN -3 -3 -1 -1 -1 -3 -1 -3 1 23 25 25 38 34 44 1 51 32 47 7 45 7 1 9 5 3 -1 1 37 55 37 59 33 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
645 D. Andersson 42 1972-12-18 187 85 Sweden Helsingborgs IF 57 57 20000 GK Right 1 3 1 Medium/Medium Normal No SUB 39.0 2011-01-01 2020.0 0.0 0.0 0.0 0.0 0.0 0.0 12.0 NaN -1 1 -1 37 -5 1 1 9 32 25 -1 -1 -3 61 25 25 11 1 64 3 34 22 -1 31 -1 -1 11 13 59 52 50 59 63 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
165854 J. Henao 43 1971-12-30 181 79 Colombia Once Caldas 65 65 90000 GK Right 1 3 1 Medium/Medium Stocky No SUB 1.0 2010-07-01 2016.0 0.0 0.0 0.0 0.0 0.0 0.0 53.0 Team Player 3 -5 5 34 5 13 3 24 48 25 52 53 59 60 57 1 72 34 60 3 32 22 1 27 11 3 1 -1 72 70 58 67 62 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
140029 O. Pérez 42 1973-02-01 172 75 Mexico Pachuca 70 70 230000 GK Right 2 3 1 Medium/Medium Stocky No GK 21.0 2015-07-01 2016.0 0.0 0.0 0.0 0.0 0.0 0.0 58.0 GK Up for Corners, GK Long Throw 13 3 15 27 1 -1 -1 15 28 20 71 66 69 73 69 18 85 0 66 11 10 -1 -1 55 24 -3 -1 -3 71 65 66 73 74 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
31834 F. Johnsen 41 1974-03-17 187 79 Norway Odds BK 66 66 0 ST Right 1 4 3 Medium/Medium Lean No SUB 11.0 2010-01-01 2015.0 39.0 70.0 52.0 56.0 41.0 62.0 0.0 Power Header 54 72 84 53 73 51 62 52 44 61 15 22 62 53 57 75 70 48 74 66 49 33 77 55 61 33 44 43 15 13 10 9 15 66 66 66 56 60 60 60 56 57 57 57 54 53 53 53 54 45 46 46 46 45 45 50 50 50 45

Nothing interesting here since these are players in maybe some very low division clubs.

Which leads us to the idea of having these statistics made per nationality of the club players are in. Let's take the 5 best football leagues in Europe, those of: England, Spain, Italy, Germany and France. In this dataset we don't have information which league each club belongs to. So we have to get this data from another source.

The clubs dataset is created manually by copy/pasting lists with teams which participated in the number one league of each country (above mentioned five) during the 2015/16 season. These lists are taken from Wikipedia.

In [30]:
clubs_data=pd.read_csv("data/Main_leagues_2016.csv",encoding="utf-8",sep="\\t")
C:\Users\mbararova\AppData\Local\Continuum\anaconda3\lib\site-packages\ipykernel_launcher.py:1: ParserWarning:

Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.

In [31]:
clubs_data
Out[31]:
Club Club_country League
0 Bournemouth England Premier League
1 Arsenal England Premier League
2 Aston Villa England Premier League
3 Chelsea England Premier League
4 Crystal Palace England Premier League
5 Everton England Premier League
6 Leicester City England Premier League
7 Liverpool England Premier League
8 Manchester City England Premier League
9 Manchester United England Premier League
10 Newcastle United England Premier League
11 Norwich City England Premier League
12 Southampton England Premier League
13 Stoke City England Premier League
14 Sunderland England Premier League
15 Swansea City England Premier League
16 Tottenham Hotspur England Premier League
17 Watford England Premier League
18 West Bromwich Albion England Premier League
19 West Ham United England Premier League
20 Atalanta Italy Serie A
21 Bologna Italy Serie A
22 Carpi Italy Serie A
23 Chievo Verona Italy Serie A
24 Empoli Italy Serie A
25 Fiorentina Italy Serie A
26 Frosinone Italy Serie A
27 Genoa Italy Serie A
28 Hellas Verona Italy Serie A
29 Inter Italy Serie A
30 Juventus Italy Serie A
31 Lazio Italy Serie A
32 Milan Italy Serie A
33 Napoli Italy Serie A
34 Palermo Italy Serie A
35 Roma Italy Serie A
36 Sampdoria Italy Serie A
37 Sassuolo Italy Serie A
38 Torino Italy Serie A
39 Udinese Italy Serie A
40 FC Augsburg Germany Bundesliga
41 Bayer 04 Leverkusen Germany Bundesliga
42 FC Bayern München Germany Bundesliga
43 Borussia Dortmund Germany Bundesliga
44 Borussia Mönchengladbach Germany Bundesliga
45 SV Darmstadt 98 Germany Bundesliga
46 Eintracht Frankfurt Germany Bundesliga
47 Hamburger SV Germany Bundesliga
48 Hannover 96 Germany Bundesliga
49 Hertha BSC Germany Bundesliga
50 TSG 1899 Hoffenheim Germany Bundesliga
51 FC Ingolstadt 04 Germany Bundesliga
52 1. FC Köln Germany Bundesliga
53 1. FSV Mainz 05 Germany Bundesliga
54 FC Schalke 04 Germany Bundesliga
55 VfB Stuttgart Germany Bundesliga
56 SV Werder Bremen Germany Bundesliga
57 VfL Wolfsburg Germany Bundesliga
58 Bilbao Athletic Spain La Liga
59 Atlético Madrid Spain La Liga
60 FC Barcelona Spain La Liga
61 RC Celta Spain La Liga
62 Deportivo de La Coruña Spain La Liga
63 SD Eibar Spain La Liga
64 RCD Espanyol Spain La Liga
65 Getafe CF Spain La Liga
66 Granada CF Spain La Liga
67 UD Las Palmas Spain La Liga
68 Levante UD Spain La Liga
69 Málaga Spain La Liga
70 Rayo Vallecano Spain La Liga
71 Real Betis Spain La Liga
72 Real Madrid Spain La Liga
73 Real Sociedad Spain La Liga
74 Sevilla FC Spain La Liga
75 Real Sporting de Gijón Spain La Liga
76 Valencia CF Spain La Liga
77 Villarreal CF Spain La Liga
78 Angers SCO France Ligue 1
79 Sporting Club de Bastia France Ligue 1
80 FC Girondins de Bordeaux France Ligue 1
81 Stade Malherbe Caen France Ligue 1
82 GFC Ajaccio France Ligue 1
83 En Avant de Guingamp France Ligue 1
84 LOSC Lille France Ligue 1
85 FC Lorient France Ligue 1
86 Olympique Lyonnais France Ligue 1
87 Olympique de Marseille France Ligue 1
88 AS Monaco France Ligue 1
89 Montpellier HSC France Ligue 1
90 FC Nantes France Ligue 1
91 OGC Nice France Ligue 1
92 Paris Saint-Germain France Ligue 1
93 Stade de Reims France Ligue 1
94 Stade Rennais FC France Ligue 1
95 AS Saint-Étienne France Ligue 1
96 Toulouse Football Club France Ligue 1
97 ESTAC Troyes France Ligue 1
In [32]:
#Count the number of teams in each league

clubs_data.groupby("League")["League"].count()
Out[32]:
League
Bundesliga        18
La Liga           20
Ligue 1           20
Premier League    20
Serie A           20
Name: League, dtype: int64

Data seems ok. But we have to check if the names of clubs coinside in both tables (most probably, not exactly). In list "missing" we will put those club names from clubs_data which are not found in players_16.

In [33]:
missing=[club for club in clubs_data["Club"].unique() if club not in (players_16["club"].unique())]
In [34]:
missing
Out[34]:
['Málaga']

This is quite strange that "Malaga" is missing in the data base. The manual search also didn't give any result. We have to live with that. One thing which was nothiced is that one and the same club has two ways of naming: "Athletic Club de Bilbao" and "Bilbao Athletic". In general, this club is known as "Athletico Bilbao", so I will rename all occurrences.

In [35]:
players_16["club"]=players_16["club"].str.replace("Athletic Club de Bilbao","Athletico Bilbao")
players_16["club"]=players_16["club"].str.replace("Bilbao Athletic" ,"Athletico Bilbao")

Here we will check if the substitution went correctly. All clubs containing "Bilbao" should be only "Athletic Bilbao".

In [36]:
players_16[(players_16["club"].str.find("Bilbao"))!=-1]["club"].unique()
Out[36]:
array(['Athletico Bilbao'], dtype=object)

Finally, we can merge the datasets and sametime make s sub dataset with players only from the major European leagues and see statistics for them.

In [37]:
euro_league_players=players_16.merge(clubs_data, left_on='club', right_on='Club')
In [38]:
euro_league_players.shape
Out[38]:
(2714, 92)
In [39]:
euro_league_players.head()
Out[39]:
short_name age dob height_cm weight_kg nationality club overall potential value_eur player_positions preferred_foot international_reputation weak_foot skill_moves work_rate body_type real_face team_position team_jersey_number joined contract_valid_until pace shooting passing dribbling defending physic gk_speed player_traits attacking_crossing attacking_finishing attacking_heading_accuracy attacking_short_passing attacking_volleys skill_dribbling skill_curve skill_fk_accuracy skill_long_passing skill_ball_control movement_acceleration movement_sprint_speed movement_agility movement_reactions movement_balance power_shot_power power_jumping power_stamina power_strength power_long_shots mentality_aggression mentality_interceptions mentality_positioning mentality_vision mentality_penalties defending_marking defending_standing_tackle defending_sliding_tackle goalkeeping_diving goalkeeping_handling goalkeeping_kicking goalkeeping_positioning goalkeeping_reflexes ls st rs lw lf cf rf rw lam cam ram lm lcm cm rcm rm lwb ldm cdm rdm rwb lb lcb cb rcb rb Club Club_country League
0 L. Messi 28 1987-06-24 170 72 Argentina FC Barcelona 94 95 111000000 RW, CF Left 5 4 4 Medium/Low Messi Yes RW 10.0 2004-07-01 2018.0 92.0 88.0 86.0 95.0 24.0 62.0 0.0 Finesse Shot, Speed Dribbler (CPU AI Only), On... 76 92 71 87 85 96 89 90 82 96 94 90 90 90 95 80 63 75 58 88 48 22 88 90 74 1 25 22 6 11 15 14 8 90 90 90 94 94 94 94 94 94 94 94 93 85 85 85 93 65 60 60 60 65 60 47 47 47 60 FC Barcelona Spain La Liga
1 L. Suárez 28 1987-01-24 182 85 Uruguay FC Barcelona 90 90 69000000 ST Right 5 4 4 High/Medium Normal Yes ST 9.0 2014-07-11 2019.0 83.0 88.0 79.0 87.0 42.0 79.0 0.0 Diver, Beat Offside Trap, Flair, Technical Dri... 77 89 79 82 89 86 86 84 64 93 88 77 86 91 60 92 69 86 76 88 78 41 94 84 85 30 45 38 27 25 31 33 37 90 90 90 90 91 91 91 90 89 89 89 88 82 82 82 88 70 68 68 68 70 67 61 61 61 67 FC Barcelona Spain La Liga
2 Neymar 23 1992-02-05 174 68 Brazil FC Barcelona 88 93 71500000 LW Right 5 5 5 High/Medium Neymar Yes LW 11.0 2013-07-01 2018.0 90.0 80.0 72.0 92.0 30.0 57.0 0.0 Diver, Flair, Technical Dribbler (CPU AI Only) 71 85 62 72 83 94 78 78 72 92 91 91 92 86 84 77 61 74 45 70 56 36 87 72 81 21 24 33 9 9 15 15 11 84 84 84 88 87 87 87 88 86 86 86 86 78 78 78 86 65 59 59 59 65 61 48 48 48 61 FC Barcelona Spain La Liga
3 Sergio Busquets 26 1988-07-16 189 76 Spain FC Barcelona 86 88 39000000 CDM Right 4 3 3 Medium/Medium Lean Yes CDM 5.0 2008-09-01 2019.0 53.0 59.0 78.0 75.0 83.0 81.0 0.0 Diver 62 60 70 87 44 74 66 68 80 83 53 48 57 82 55 61 66 86 77 54 86 86 77 85 60 88 88 81 5 8 13 9 13 71 71 71 73 75 75 75 73 78 78 78 76 83 83 83 76 80 86 86 86 80 80 83 83 83 80 FC Barcelona Spain La Liga
4 Piqué 28 1987-02-02 193 85 Spain FC Barcelona 85 86 29500000 CB Right 4 3 2 High/Medium Normal Yes RCB 3.0 2008-07-01 2019.0 64.0 61.0 70.0 64.0 86.0 76.0 0.0 Long Passer (CPU AI Only) 64 70 82 82 57 68 73 43 76 76 56 67 57 81 44 71 74 69 83 60 68 88 66 62 69 86 87 83 10 11 14 15 8 70 70 70 68 69 69 69 68 70 70 70 70 76 76 76 70 78 83 83 83 78 80 85 85 85 80 FC Barcelona Spain La Liga
In [40]:
draw_graphs(dataset=euro_league_players,title="Average age per league",
            analyzed_feature="age",groupby_feature="League",
            asc=False,xtitle="League",ytitle="Age",
            stat_function="mean",
            orient='v',marker_color="lightblue")

It is interesting to see that England was the country with youngest players (average age is: 23.78) but the average age in Premier League is quite high 25.21.

Below we will show boxplots of ages per league.

In [41]:
league_and_age=euro_league_players[["League","age"]]
fig = px.box(league_and_age, x="League", y="age",color="League",title="Age per League")

fig.show()

Let's check again the outliers because it is interesting to see who is playeing at the age of 42 and 40 in such serious tournaments. We will show the oldest footballer in each league.

In [42]:
#leagues_list=["England","Sweden", "Colombia","Mexico","Norway"]
euro_outliers=pd.DataFrame()
for league in euro_league_players["League"].unique():
    player_league=league
    league_max_age=euro_league_players[euro_league_players["League"]==league]["age"].max()
    euro_outliers=euro_outliers.append(euro_league_players[(euro_league_players["League"]==league) & (euro_league_players["age"]==league_max_age)])# & players_top_20_nations["age"]==nationality_max_age])
euro_outliers
Out[42]:
short_name age dob height_cm weight_kg nationality club overall potential value_eur player_positions preferred_foot international_reputation weak_foot skill_moves work_rate body_type real_face team_position team_jersey_number joined contract_valid_until pace shooting passing dribbling defending physic gk_speed player_traits attacking_crossing attacking_finishing attacking_heading_accuracy attacking_short_passing attacking_volleys skill_dribbling skill_curve skill_fk_accuracy skill_long_passing skill_ball_control movement_acceleration movement_sprint_speed movement_agility movement_reactions movement_balance power_shot_power power_jumping power_stamina power_strength power_long_shots mentality_aggression mentality_interceptions mentality_positioning mentality_vision mentality_penalties defending_marking defending_standing_tackle defending_sliding_tackle goalkeeping_diving goalkeeping_handling goalkeeping_kicking goalkeeping_positioning goalkeeping_reflexes ls st rs lw lf cf rf rw lam cam ram lm lcm cm rcm rm lwb ldm cdm rdm rwb lb lcb cb rcb rb Club Club_country League
2418 Valerón 40 1975-06-17 186 76 Spain UD Las Palmas 74 74 0 CAM, CM Right 2 4 4 Medium/Low Lean Yes SUB 21.0 2013-07-15 2016.0 34.0 59.0 80.0 76.0 23.0 36.0 0.0 Injury Free, Playmaker (CPU AI Only), Swerve P... 73 61 64 89 60 80 73 68 74 85 30 32 49 82 65 50 27 19 43 64 35 28 65 89 67 3 15 3 8 9 15 10 9 63 63 63 69 70 70 70 69 74 74 74 69 70 70 70 69 47 50 50 50 47 42 36 36 36 42 UD Las Palmas Spain La Liga
1774 A. Manninger 38 1977-06-04 189 85 Austria FC Augsburg 72 72 350000 GK Right 2 2 1 Medium/Medium Normal Yes SUB 1.0 2012-11-21 2016.0 0.0 0.0 0.0 0.0 0.0 0.0 40.0 Puncher, Team Player -1 -1 1 23 3 1 5 5 9 0 39 40 48 77 45 23 70 31 74 -3 40 7 -7 3 9 -3 3 5 73 73 65 73 75 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 FC Augsburg Germany Bundesliga
1394 M. Schwarzer 42 1972-10-06 194 95 Australia Leicester City 73 73 500000 GK Right 2 3 1 Medium/Medium Normal No SUB 32.0 2015-01-06 2016.0 0.0 0.0 0.0 0.0 0.0 0.0 48.0 NaN 1 -3 3 25 9 1 3 -3 24 9 50 46 36 76 43 30 57 -7 70 11 41 23 13 42 28 9 13 15 69 73 68 79 71 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Leicester City England Premier League
2603 B. Nivet 38 1977-01-02 178 75 France ESTAC Troyes 73 73 300000 CAM, CM Right 2 3 3 Medium/Medium Normal No SUB 10.0 2012-06-12 2016.0 59.0 70.0 74.0 72.0 55.0 61.0 0.0 Playmaker (CPU AI Only), Team Player 63 73 59 79 65 75 74 74 74 80 54 60 64 71 72 72 24 59 70 68 59 55 70 82 77 54 60 46 12 8 9 15 8 69 69 69 70 71 71 71 70 73 73 73 70 72 72 72 70 61 65 65 65 61 60 58 58 58 60 ESTAC Troyes France Ligue 1
857 L. Castellazzi 39 1975-07-19 192 89 Italy Torino 72 72 350000 GK Left 2 2 1 Medium/Medium Lean No RES 13.0 2014-09-01 2016.0 0.0 0.0 0.0 0.0 0.0 0.0 33.0 Puncher -1 -3 -3 32 -3 7 13 1 28 23 9 13 53 66 55 5 59 31 76 5 30 23 3 45 6 1 -1 7 76 76 66 84 66 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Torino Italy Serie A

$$$

And now let's finally see the money part. First we will check the major leagues, after that will make the analysis on the whole dataset.

In [43]:
euro_leagues_value=euro_league_players.groupby("League")["value_eur"].sum().sort_values()
In [44]:
draw_graphs(dataset=euro_league_players,title="Total value per league",
            analyzed_feature="value_eur",groupby_feature="League",
            asc=True,xtitle="Total Value",ytitle="League",
            stat_function="sum",
            orient='h',marker_color="lightgreen")
In [45]:
draw_graphs(dataset=euro_league_players,title="Average player value per league",
            analyzed_feature="value_eur",groupby_feature="League",
            asc=True,xtitle="Average Value",ytitle="Age",
            stat_function="mean",
            orient='h',marker_color="violet")
In [46]:
draw_graphs(dataset=euro_league_players,title="Total value per club",
            analyzed_feature="value_eur",groupby_feature="club",
            asc=False,xtitle="Club",ytitle="Total Value",
            stat_function="sum",
            orient='v',num_recs=20,marker_color="lightyellow")
In [47]:
draw_graphs(dataset=euro_league_players,title="Average value per club",
            analyzed_feature="value_eur",groupby_feature="club",
            asc=False,xtitle="Club",ytitle="Average Value",
            stat_function="mean",
            orient='v',num_recs=20,marker_color="orange")
In [48]:
draw_graphs(dataset=euro_league_players,title="Maximum player value per club",
            analyzed_feature="value_eur",groupby_feature="club",
            asc=False,xtitle="Club",ytitle="Maximum Value",
            stat_function="max",
            orient='v',num_recs=20,marker_color="rosybrown")
In [49]:
players_top_20_nations
Out[49]:
short_name age dob height_cm weight_kg nationality club overall potential value_eur player_positions preferred_foot international_reputation weak_foot skill_moves work_rate body_type real_face team_position team_jersey_number joined contract_valid_until pace shooting passing dribbling defending physic gk_speed player_traits attacking_crossing attacking_finishing attacking_heading_accuracy attacking_short_passing attacking_volleys skill_dribbling skill_curve skill_fk_accuracy skill_long_passing skill_ball_control movement_acceleration movement_sprint_speed movement_agility movement_reactions movement_balance power_shot_power power_jumping power_stamina power_strength power_long_shots mentality_aggression mentality_interceptions mentality_positioning mentality_vision mentality_penalties defending_marking defending_standing_tackle defending_sliding_tackle goalkeeping_diving goalkeeping_handling goalkeeping_kicking goalkeeping_positioning goalkeeping_reflexes ls st rs lw lf cf rf rw lam cam ram lm lcm cm rcm rm lwb ldm cdm rdm rwb lb lcb cb rcb rb
sofifa_id
158023 L. Messi 28 1987-06-24 170 72 Argentina FC Barcelona 94 95 111000000 RW, CF Left 5 4 4 Medium/Low Messi Yes RW 10.0 2004-07-01 2018.0 92.0 88.0 86.0 95.0 24.0 62.0 0.0 Finesse Shot, Speed Dribbler (CPU AI Only), On... 76 92 71 87 85 96 89 90 82 96 94 90 90 90 95 80 63 75 58 88 48 22 88 90 74 1 25 22 6 11 15 14 8 90 90 90 94 94 94 94 94 94 94 94 93 85 85 85 93 65 60 60 60 65 60 47 47 47 60
20801 Cristiano Ronaldo 30 1985-02-05 185 80 Portugal Real Madrid 93 93 85500000 LW, LM Right 5 4 5 High/Low C. Ronaldo Yes LM 7.0 2009-07-01 2018.0 92.0 93.0 80.0 91.0 33.0 78.0 0.0 Power Free-Kick, Flair, Long Shot Taker (CPU A... 81 95 86 80 87 93 88 75 72 90 91 92 87 94 61 94 94 85 79 93 61 34 95 81 85 22 31 23 7 11 15 14 11 94 94 94 93 94 94 94 93 91 91 91 91 83 83 83 91 67 63 63 63 67 63 55 55 55 63
9014 A. Robben 31 1984-01-23 180 80 Netherlands FC Bayern München 90 90 56000000 RM, LM, RW Left 5 2 4 High/Low Normal Yes SUB 10.0 2009-08-28 2017.0 92.0 86.0 82.0 92.0 32.0 64.0 0.0 Diver, Injury Prone, Avoids Using Weaker Foot,... 80 85 50 84 86 93 87 83 72 88 91 91 89 91 91 86 61 74 65 90 47 39 89 84 80 29 26 26 10 8 11 5 15 87 87 87 92 91 91 91 92 91 91 91 90 83 83 83 90 68 63 63 63 68 62 50 50 50 62
167495 M. Neuer 29 1986-03-27 193 92 Germany FC Bayern München 90 90 58000000 GK Right 5 4 1 Medium/Medium Normal Yes GK 1.0 2011-07-01 2019.0 0.0 0.0 0.0 0.0 0.0 0.0 60.0 GK Long Throw, 1-on-1 Rush 5 1 25 54 -3 7 3 -3 53 31 58 61 43 88 35 8 78 44 83 7 29 30 -1 90 37 -5 -5 -3 82 89 91 90 86 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
41236 Z. Ibrahimović 33 1981-10-03 195 95 Sweden Paris Saint-Germain 89 89 40500000 ST Right 5 4 4 Medium/Low Normal Yes ST 10.0 2012-07-01 2016.0 73.0 90.0 81.0 85.0 31.0 86.0 0.0 Power Free-Kick, Leadership, Flair, Long Shot ... 76 90 76 84 92 87 80 80 76 90 70 71 86 85 41 93 72 72 93 88 84 20 86 83 91 5 41 27 13 15 10 9 12 89 89 89 87 89 89 89 87 88 88 88 86 81 81 81 86 63 63 63 63 63 59 56 56 56 59
190871 Neymar 23 1992-02-05 174 68 Brazil FC Barcelona 88 93 71500000 LW Right 5 5 5 High/Medium Neymar Yes LW 11.0 2013-07-01 2018.0 90.0 80.0 72.0 92.0 30.0 57.0 0.0 Diver, Flair, Technical Dribbler (CPU AI Only) 71 85 62 72 83 94 78 78 72 92 91 91 92 86 84 77 61 74 45 70 56 36 87 72 81 21 24 33 9 9 15 15 11 84 84 84 88 87 87 87 88 86 86 86 86 78 78 78 86 65 59 59 59 65 61 48 48 48 61
164240 Thiago Silva 30 1984-09-22 183 79 Brazil Paris Saint-Germain 88 88 38000000 CB Right 4 3 3 High/High Normal Yes RCB 2.0 2012-07-01 2018.0 74.0 57.0 73.0 73.0 90.0 79.0 0.0 Leadership, Long Passer (CPU AI Only), Power H... 60 38 82 79 63 68 61 73 81 80 69 72 75 85 68 78 96 68 81 71 79 91 59 74 71 92 92 90 9 12 5 9 10 70 70 70 71 72 72 72 71 74 74 74 73 79 79 79 73 82 86 86 86 82 84 88 88 88 84
168542 David Silva 29 1986-01-08 170 67 Spain Manchester City 88 88 50500000 CAM, LM Left 4 2 4 High/Low Normal Yes SUB 21.0 2010-07-14 2019.0 73.0 74.0 89.0 89.0 32.0 59.0 0.0 Avoids Using Weaker Foot, Flair, Playmaker (CP... 91 71 50 96 80 87 83 77 85 91 76 65 93 84 88 71 66 68 59 78 51 41 84 96 77 23 30 29 1 1 1 1 1 78 78 78 86 85 85 85 86 88 88 88 86 84 84 84 86 65 64 64 64 65 60 49 49 49 60
198710 J. Rodríguez 23 1991-07-12 180 75 Colombia Real Madrid 87 93 62500000 CAM, CM, RM Left 4 3 4 Medium/Medium Normal Yes CAM 10.0 2014-07-22 2020.0 78.0 86.0 85.0 85.0 43.0 72.0 0.0 Flair, Playmaker (CPU AI Only), Technical Drib... 95 90 68 85 100 88 79 89 76 85 77 77 82 85 74 93 55 75 69 91 74 58 86 87 81 25 58 30 15 15 15 5 14 84 84 84 87 86 86 86 87 87 87 87 86 83 83 83 86 69 68 68 68 69 65 58 58 58 65
182521 T. Kroos 25 1990-01-04 182 78 Germany Real Madrid 87 90 54500000 CM, CDM Right 4 5 3 Medium/Medium Normal Yes RDM 8.0 2014-07-17 2020.0 56.0 81.0 88.0 82.0 66.0 69.0 0.0 Long Shot Taker (CPU AI Only), Playmaker (CPU ... 84 76 54 88 80 84 80 83 89 87 57 50 67 85 46 87 54 78 70 90 54 80 77 88 73 60 76 59 10 11 13 7 10 78 78 78 82 82 82 82 82 85 85 85 82 87 87 87 82 77 79 79 79 77 74 70 70 70 74
183907 J. Boateng 26 1988-09-03 192 90 Germany FC Bayern München 87 89 45000000 CB Right 4 4 2 Medium/Medium Normal Yes CB 17.0 2011-07-14 2018.0 79.0 50.0 69.0 68.0 87.0 84.0 0.0 Long Passer (CPU AI Only), Giant Throw-in 69 34 88 73 48 67 56 36 74 70 74 81 51 82 55 80 74 77 90 58 82 84 47 75 46 84 92 92 7 12 15 6 5 68 68 68 68 68 68 68 68 69 69 69 71 74 74 74 71 82 83 83 83 82 84 87 87 87 84
188545 R. Lewandowski 26 1988-08-21 185 79 Poland FC Bayern München 87 89 55000000 ST Right 4 4 3 Medium/Medium Normal Yes ST 9.0 2014-07-01 2019.0 80.0 85.0 74.0 84.0 38.0 80.0 0.0 Injury Free, Chip Shot (CPU AI Only) 62 88 83 81 82 84 77 68 64 87 79 81 72 88 75 84 83 83 83 80 80 39 87 77 77 25 42 13 15 6 12 8 10 87 87 87 84 86 86 86 84 85 85 85 83 79 79 79 83 63 64 64 64 63 60 58 58 58 60
176635 M. Özil 26 1988-10-15 183 76 Germany Arsenal 87 88 52500000 CAM, LW Left 4 2 4 Medium/Low Lean Yes CAM 11.0 2013-09-02 2018.0 72.0 74.0 85.0 86.0 24.0 57.0 0.0 Finesse Shot, Flair, Playmaker (CPU AI Only), ... 80 74 54 87 77 86 84 79 80 90 77 65 84 87 64 70 51 64 57 75 54 24 85 92 76 22 7 13 6 14 10 6 14 78 78 78 85 84 84 84 85 87 87 87 84 80 80 80 84 59 58 58 58 59 54 43 43 43 54
121939 P. Lahm 31 1983-11-11 170 66 Germany FC Bayern München 87 87 29500000 CDM, RB, CM Right 4 3 3 Medium/High Normal Yes RM 21.0 2002-11-01 2018.0 75.0 56.0 84.0 85.0 87.0 66.0 0.0 Dives Into Tackles (CPU AI Only), Leadership, ... 84 47 62 88 66 82 77 59 84 87 77 73 82 90 94 57 72 82 59 65 58 94 69 82 71 87 86 95 11 12 5 14 5 71 71 71 80 78 78 78 80 82 82 82 82 86 86 86 82 89 87 87 87 89 88 83 83 83 88
138956 G. Chiellini 30 1984-08-14 186 76 Italy Juventus 87 87 32500000 CB Left 4 2 2 Low/High Normal Yes LCB 3.0 2005-07-01 2018.0 77.0 47.0 56.0 58.0 90.0 84.0 0.0 Long Throw-in, Avoids Using Weaker Foot, Dives... 68 38 88 61 61 56 60 31 71 56 77 79 71 82 78 77 88 63 88 48 91 87 34 55 50 92 92 95 3 3 2 4 3 61 61 61 58 57 57 57 58 56 56 56 60 62 62 62 60 77 77 77 77 77 80 87 87 87 80
153079 S. Agüero 27 1988-06-02 172 74 Argentina Manchester City 87 87 47500000 ST Right 4 4 4 High/Medium Normal Yes ST 10.0 2011-07-28 2019.0 89.0 87.0 77.0 89.0 23.0 68.0 0.0 Injury Prone, Beat Offside Trap, Flair, Techni... 70 90 68 82 85 89 82 72 63 89 92 88 86 88 90 85 76 69 72 82 57 24 90 83 83 1 20 -1 13 15 6 11 14 87 87 87 88 89 89 89 88 88 88 88 86 78 78 78 86 59 56 56 56 59 54 45 45 45 54
155862 Sergio Ramos 29 1986-03-30 183 75 Spain Real Madrid 87 87 34000000 CB Right 4 3 3 High/Medium Normal Yes LCB 4.0 2005-08-01 2020.0 79.0 63.0 72.0 69.0 87.0 81.0 0.0 Leadership, Power Header 74 61 86 76 77 62 73 72 70 83 77 79 80 82 60 83 91 80 80 55 83 89 52 63 68 85 89 90 11 8 9 7 11 73 73 73 71 71 71 71 71 71 71 71 73 75 75 75 73 83 84 84 84 83 85 87 87 87 85
156616 F. Ribéry 32 1983-04-07 170 72 France FC Bayern München 87 87 34000000 LM Right 4 4 5 High/Medium Normal Yes SUB 7.0 2007-07-01 2017.0 87.0 77.0 84.0 91.0 25.0 59.0 0.0 Injury Prone, Flair, Technical Dribbler (CPU A... 81 77 41 85 79 90 82 81 72 91 85 85 88 85 90 76 49 56 60 73 52 36 83 88 80 1 25 26 15 6 9 7 10 80 80 80 88 87 87 87 88 88 88 88 87 80 80 80 87 64 60 60 60 64 58 45 45 45 58
162895 Cesc Fàbregas 28 1987-05-04 175 74 Spain Chelsea 87 87 43500000 CM, CAM Right 4 3 3 High/Medium Normal Yes RDM 4.0 2014-07-01 2019.0 63.0 78.0 90.0 81.0 64.0 65.0 0.0 Long Passer (CPU AI Only), Playmaker (CPU AI O... 89 78 74 94 81 81 77 81 93 87 63 56 65 83 77 77 68 79 64 78 45 61 79 93 80 62 66 62 6 10 8 15 15 80 80 80 83 84 84 84 83 86 86 86 85 87 87 87 85 77 77 77 77 77 74 69 69 69 74
195864 P. Pogba 22 1993-03-15 188 80 France Juventus 86 92 53000000 CM Right 4 4 4 High/Medium Lean Yes LCM 10.0 2012-07-01 2019.0 77.0 80.0 82.0 86.0 74.0 88.0 0.0 Dives Into Tackles (CPU AI Only), Flair, Long ... 79 70 60 85 84 88 81 95 82 88 75 81 71 93 62 93 83 87 92 91 78 73 87 87 85 80 76 85 5 6 2 4 3 84 84 84 85 86 86 86 85 86 86 86 85 86 86 86 85 82 83 83 83 82 81 81 81 81 81
193080 De Gea 24 1990-11-07 193 82 Spain Manchester United 86 89 42500000 GK Right 3 3 1 Medium/Medium Lean Yes GK 1.0 2011-07-01 2019.0 0.0 0.0 0.0 0.0 0.0 0.0 53.0 Puncher 9 1 21 31 1 1 21 13 32 31 51 55 57 79 43 31 67 25 64 -1 38 30 -1 9 40 1 21 1 88 81 86 85 89 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
165153 K. Benzema 27 1987-12-19 187 79 France Real Madrid 86 88 46500000 ST Right 4 4 4 Medium/Low Normal Yes ST 9.0 2009-07-01 2019.0 83.0 84.0 76.0 81.0 22.0 75.0 0.0 Beat Offside Trap, Finesse Shot 75 88 78 88 77 82 79 73 47 86 81 84 71 83 49 83 69 78 78 78 65 22 87 85 82 1 5 -1 13 11 5 5 7 86 86 86 85 86 86 86 85 85 85 85 83 74 74 74 83 58 54 54 54 58 54 46 46 46 54
178603 M. Hummels 26 1988-12-16 191 92 Germany Borussia Dortmund 86 88 39000000 CB Right 4 3 2 High/Medium Normal Yes LCB 15.0 2009-07-01 2017.0 64.0 58.0 74.0 70.0 88.0 77.0 0.0 Avoids Using Weaker Foot, Leadership, Long Pas... 64 53 89 76 69 68 68 45 84 77 61 65 56 85 57 71 70 67 86 51 71 92 56 80 64 88 84 86 15 6 10 5 6 71 71 71 70 71 71 71 70 73 73 73 72 78 78 78 72 80 84 84 84 80 82 86 86 86 82
184941 A. Sánchez 26 1988-12-19 169 62 Chile Arsenal 86 88 47000000 LW, RW Right 4 3 4 High/High Normal Yes LW 17.0 2014-07-10 2019.0 87.0 83.0 78.0 88.0 39.0 73.0 0.0 Flair, Technical Dribbler (CPU AI Only) 76 85 60 80 78 88 78 77 73 87 88 85 91 84 87 82 72 86 64 80 84 42 87 78 81 30 39 35 10 10 15 12 13 83 83 83 86 86 86 86 86 85 85 85 85 80 80 80 85 68 65 65 65 68 64 55 55 55 64
188350 M. Reus 26 1989-05-31 180 75 Germany Borussia Dortmund 86 88 45500000 LM, CAM Right 3 4 4 Medium/Medium Lean Yes SUB 11.0 2012-07-01 2019.0 90.0 84.0 85.0 86.0 39.0 64.0 0.0 Injury Prone, Speed Dribbler (CPU AI Only) 89 81 51 88 78 86 90 87 78 85 89 90 89 87 82 83 70 70 65 83 47 63 87 83 89 35 39 48 12 12 13 13 11 82 82 82 87 86 86 86 87 87 87 87 86 81 81 81 86 69 65 65 65 69 65 53 53 53 65
189511 Sergio Busquets 26 1988-07-16 189 76 Spain FC Barcelona 86 88 39000000 CDM Right 4 3 3 Medium/Medium Lean Yes CDM 5.0 2008-09-01 2019.0 53.0 59.0 78.0 75.0 83.0 81.0 0.0 Diver 62 60 70 87 44 74 66 68 80 83 53 48 57 82 55 61 66 86 77 54 86 86 77 85 60 88 88 81 5 8 13 9 13 71 71 71 73 75 75 75 73 78 78 78 76 83 83 83 76 80 86 86 86 80 80 83 83 83 80
189596 T. Müller 25 1989-09-13 186 75 Germany FC Bayern München 86 88 47500000 CF, RM, CAM, ST Right 4 4 3 High/High Lean Yes CF 25.0 2008-08-10 2019.0 77.0 84.0 80.0 79.0 46.0 72.0 0.0 Injury Free, Finesse Shot, Outside Foot Shot 74 90 84 81 83 78 81 58 77 80 73 73 72 91 73 70 81 90 68 80 57 58 94 85 91 32 41 44 6 7 11 14 14 85 85 85 85 86 86 86 85 85 85 85 85 82 82 82 85 71 68 68 68 71 68 60 60 60 68
179844 Diego Costa 26 1988-10-07 188 85 Spain Chelsea 86 87 46500000 ST Right 4 4 3 High/Medium Normal Yes ST 19.0 2014-07-15 2019.0 82.0 83.0 63.0 79.0 40.0 88.0 0.0 Diver 63 90 82 65 81 82 58 59 48 82 79 85 63 85 42 83 60 85 91 69 90 40 89 74 76 28 39 34 11 13 12 8 11 86 86 86 81 83 83 83 81 79 79 79 79 72 72 72 79 63 61 61 61 63 61 59 59 59 61
54050 W. Rooney 29 1985-10-24 176 83 England Manchester United 86 86 38500000 ST, CM, CAM Right 4 4 3 Medium/High Stocky Yes CAM 10.0 2004-08-31 2019.0 75.0 86.0 81.0 81.0 47.0 87.0 0.0 Leadership, Long Shot Taker (CPU AI Only) 78 86 80 80 89 80 84 84 85 85 74 73 77 90 77 88 82 89 85 85 89 69 89 83 81 29 54 37 10 11 13 8 7 86 86 86 84 85 85 85 84 85 85 85 84 83 83 83 84 70 71 71 71 70 67 64 64 64 67
121944 B. Schweinsteiger 30 1984-08-01 183 79 Germany Manchester United 86 86 35000000 CM, CDM Right 4 3 3 High/High Normal Yes SUB 31.0 2015-07-13 2018.0 52.0 79.0 85.0 79.0 77.0 78.0 0.0 Injury Prone, Playmaker (CPU AI Only) 77 76 79 86 81 77 82 78 87 80 44 42 72 87 47 82 84 66 78 79 80 86 78 86 85 67 76 75 14 14 13 13 11 80 80 80 80 81 81 81 80 83 83 83 80 86 86 86 80 80 84 84 84 80 79 81 81 81 79
143001 C. Tévez 31 1984-02-05 173 71 Argentina Boca Juniors 86 86 34500000 ST, CF Right 3 3 3 High/High Stocky Yes CAM 10.0 2015-07-13 2018.0 87.0 88.0 76.0 88.0 45.0 84.0 0.0 Finesse Shot, Long Shot Taker (CPU AI Only), O... 73 89 60 76 88 88 84 86 58 91 93 85 83 92 91 94 81 93 81 92 85 34 92 77 81 43 51 51 4 2 3 2 4 86 86 86 87 88 88 88 87 86 86 86 85 79 79 79 85 70 67 67 67 70 67 61 61 61 67
181872 A. Vidal 28 1987-05-22 180 75 Chile FC Bayern München 86 86 37500000 CM, CAM, CDM Right 4 4 3 High/High Normal Yes RDM 23.0 2015-07-28 2019.0 77.0 79.0 80.0 81.0 83.0 83.0 0.0 Dives Into Tackles (CPU AI Only), Long Shot Ta... 76 74 76 83 78 82 76 71 81 83 77 75 71 90 76 86 83 93 75 82 91 90 81 84 86 77 92 88 4 2 4 2 4 82 82 82 82 83 83 83 82 83 83 83 83 86 86 86 83 86 87 87 87 86 86 85 85 85 86
152729 Piqué 28 1987-02-02 193 85 Spain FC Barcelona 85 86 29500000 CB Right 4 3 2 High/Medium Normal Yes RCB 3.0 2008-07-01 2019.0 64.0 61.0 70.0 64.0 86.0 76.0 0.0 Long Passer (CPU AI Only) 64 70 82 82 57 68 73 43 76 76 56 67 57 81 44 71 74 69 83 60 68 88 66 62 69 86 87 83 10 11 14 15 8 70 70 70 68 69 69 69 68 70 70 70 70 76 76 76 70 78 83 83 83 78 80 85 85 85 80
13732 J. Terry 34 1980-12-07 187 90 England Chelsea 85 85 10500000 CB Right 3 4 2 Medium/High Normal Yes SUB 26.0 1998-10-28 2016.0 34.0 47.0 57.0 52.0 87.0 81.0 0.0 Leadership, Power Header, One Club Player 42 46 90 69 55 45 44 31 64 65 32 24 44 85 46 61 81 67 88 33 88 89 38 59 52 89 89 87 14 5 6 15 8 58 58 58 51 55 55 55 51 56 56 56 54 64 64 64 54 69 78 78 78 69 72 85 85 85 72
146562 Santi Cazorla 30 1984-12-13 168 66 Spain Arsenal 85 85 31000000 CAM, CM, LM Right 3 5 4 Medium/Medium Stocky Yes LCM 19.0 2012-08-07 2017.0 73.0 78.0 85.0 87.0 57.0 64.0 0.0 Flair, Long Shot Taker (CPU AI Only), Technica... 91 77 53 86 67 87 86 78 84 86 79 68 86 85 91 78 71 79 60 82 63 55 82 85 80 58 62 48 6 9 5 7 15 78 78 78 84 83 83 83 84 85 85 85 84 82 82 82 84 72 72 72 72 72 69 62 62 62 69
167948 H. Lloris 28 1986-12-26 188 78 France Tottenham Hotspur 85 85 29000000 GK Left 3 1 1 Medium/Medium Lean Yes GK 1.0 2012-08-01 2019.0 0.0 0.0 0.0 0.0 0.0 0.0 64.0 Puncher 1 -5 -5 27 -3 -5 -3 -5 31 34 65 63 55 84 54 23 74 41 43 3 31 27 -5 7 40 -1 -5 11 88 82 68 81 88 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
171919 Naldo 32 1982-09-10 198 89 Brazil VfL Wolfsburg 85 85 19500000 CB Right 3 4 2 High/Medium Lean No RCB 25.0 2012-07-19 2016.0 73.0 69.0 64.0 63.0 88.0 76.0 0.0 Power Free-Kick, Power Header 51 63 93 76 59 60 45 76 77 70 67 79 59 82 42 92 49 64 86 89 76 100 53 60 61 87 90 88 14 10 14 8 14 73 73 73 65 69 69 69 65 68 68 68 65 72 72 72 65 76 80 80 80 76 79 85 85 85 79
183898 A. Di María 27 1988-02-14 180 75 Argentina Paris Saint-Germain 85 85 34000000 CAM, CM, RM Left 4 2 4 High/Medium Lean Yes LW 11.0 2015-08-06 2019.0 88.0 79.0 83.0 86.0 49.0 70.0 0.0 Diver, Avoids Using Weaker Foot, Dives Into Ta... 89 75 53 81 77 87 83 72 81 86 88 88 90 80 79 87 72 78 64 79 76 29 84 83 73 37 63 61 10 7 11 12 11 81 81 81 86 85 85 85 86 85 85 85 86 81 81 81 86 73 69 69 69 73 70 61 61 61 70
197781 Isco 23 1992-04-21 176 74 Spain Real Madrid 84 89 39000000 CAM, CM, LM Right 3 3 4 High/Medium Normal Yes SUB 22.0 2013-07-03 2018.0 75.0 76.0 81.0 87.0 40.0 63.0 0.0 Selfish, Finesse Shot, Playmaker (CPU AI Only)... 72 76 57 83 69 87 88 74 77 89 76 72 80 79 84 71 64 77 59 80 58 53 75 83 76 25 63 48 10 8 12 15 6 77 77 77 82 82 82 82 82 84 84 84 82 80 80 80 82 65 64 64 64 65 61 53 53 53 61
199556 M. Verratti 22 1992-11-05 165 60 Italy Paris Saint-Germain 84 89 38000000 CM, CDM Right 3 4 4 High/High Normal Yes RCM 6.0 2012-07-01 2019.0 68.0 58.0 83.0 87.0 78.0 69.0 0.0 Long Passer (CPU AI Only), Playmaker (CPU AI O... 74 58 55 91 64 86 70 62 86 85 70 57 87 90 94 66 64 79 49 58 89 87 71 84 64 78 84 74 12 12 15 15 10 70 70 70 78 77 77 77 78 81 81 81 80 84 84 84 80 81 84 84 84 81 79 77 77 77 79
188152 Oscar 23 1991-09-09 179 67 Brazil Chelsea 84 88 38000000 CAM, RM Right 3 3 4 Medium/High Lean Yes CAM 8.0 2012-07-26 2019.0 79.0 75.0 81.0 84.0 43.0 47.0 0.0 Beat Offside Trap, Long Shot Taker (CPU AI Onl... 70 75 54 85 63 84 77 77 82 85 80 75 86 81 80 76 66 76 36 77 31 34 81 85 68 37 50 47 12 10 15 12 12 76 76 76 82 82 82 82 82 84 84 84 82 80 80 80 82 66 63 63 63 66 63 50 50 50 63
189242 Coutinho 23 1992-06-12 171 68 Brazil Liverpool 84 88 38000000 CAM, LW, CM Right 3 4 4 High/Low Lean Yes CAM 10.0 2013-01-30 2020.0 82.0 74.0 81.0 87.0 33.0 56.0 0.0 Finesse Shot, Flair, Long Shot Taker (CPU AI O... 74 70 47 85 75 87 84 72 78 88 89 79 91 75 91 87 59 73 61 87 35 30 76 87 64 24 38 35 12 7 9 14 6 75 75 75 83 82 82 82 83 84 84 84 82 78 78 78 82 63 59 59 59 63 58 46 46 46 58
189332 Jordi Alba 26 1989-03-21 170 68 Spain FC Barcelona 84 88 30000000 LB Left 3 3 3 High/Medium Normal Yes LB 18.0 2012-07-01 2020.0 92.0 69.0 75.0 82.0 80.0 75.0 0.0 NaN 83 73 58 76 60 80 77 64 69 85 93 92 89 79 87 64 81 88 67 66 73 82 77 68 59 82 86 85 13 15 13 6 13 76 76 76 81 78 78 78 81 79 79 79 81 78 78 78 81 84 80 80 80 84 84 79 79 79 84
192318 M. Götze 23 1992-06-03 176 72 Germany FC Bayern München 84 88 38000000 CAM, LM, CF, CM Right 3 4 4 Medium/Medium Normal Yes SUB 19.0 2013-07-01 2017.0 72.0 73.0 81.0 88.0 32.0 62.0 0.0 Flair, Technical Dribbler (CPU AI Only) 76 75 53 84 83 86 80 74 74 90 73 61 87 83 83 66 70 65 64 58 48 53 82 83 65 7 29 26 14 7 12 6 10 78 78 78 83 83 83 83 83 84 84 84 83 79 79 79 83 63 61 61 61 63 58 48 48 48 58
192563 B. Leno 23 1992-03-04 190 82 Germany Bayer 04 Leverkusen 84 88 32500000 GK Right 2 4 1 Medium/Medium Lean No GK 1.0 2012-01-01 2018.0 0.0 0.0 0.0 0.0 0.0 0.0 49.0 NaN -7 -7 1 38 -5 7 -7 -9 33 16 46 52 52 85 44 3 73 43 68 3 28 22 -11 81 23 -9 5 11 85 86 81 83 86 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
193301 A. Lacazette 24 1991-05-28 175 73 France Olympique Lyonnais 84 88 39000000 ST, RW Right 2 4 4 High/Medium Normal Yes RS 10.0 2009-07-01 2019.0 88.0 85.0 74.0 85.0 34.0 73.0 0.0 Diver, Flair, Speed Dribbler (CPU AI Only) 68 90 77 82 85 85 74 73 72 85 88 88 89 89 83 87 81 71 74 78 70 40 88 76 82 21 29 30 11 6 9 5 6 84 84 84 83 84 84 84 83 82 82 82 81 75 75 75 81 61 59 59 59 61 58 52 52 52 58
191180 J. Pastore 26 1989-06-20 187 78 Argentina Paris Saint-Germain 84 87 34500000 CM, LW, RW Right 3 4 4 High/Medium Lean Yes SUB 27.0 2011-08-01 2019.0 74.0 77.0 84.0 86.0 58.0 68.0 0.0 Flair, Playmaker (CPU AI Only), Technical Drib... 78 75 63 88 78 91 83 69 84 88 70 75 85 89 58 78 49 72 66 78 65 71 82 90 75 49 62 51 14 9 6 10 12 79 79 79 83 83 83 83 83 85 85 85 83 84 84 84 83 73 74 74 74 73 70 65 65 65 70
167664 G. Higuaín 27 1987-12-10 184 82 Argentina Napoli 84 86 34500000 ST Right 3 4 3 High/Medium Normal Yes LS 9.0 2013-07-27 2018.0 80.0 85.0 68.0 81.0 22.0 71.0 0.0 Outside Foot Shot 72 89 78 70 85 83 72 62 55 85 79 80 72 84 60 85 73 68 80 78 50 13 89 70 71 5 5 -5 5 12 7 5 10 84 84 84 81 83 83 83 81 79 79 79 79 69 69 69 79 54 49 49 49 54 50 43 43 43 50
170890 B. Matuidi 28 1987-04-09 175 70 France Paris Saint-Germain 84 85 25500000 CDM, CM Left 3 3 2 High/High Lean Yes LCM 14.0 2011-07-01 2018.0 75.0 67.0 77.0 75.0 83.0 81.0 0.0 Injury Free, Team Player 75 65 73 84 72 73 65 52 79 77 71 73 73 83 79 74 85 94 71 66 84 86 70 74 56 82 84 83 8 11 5 10 14 74 74 74 76 75 75 75 76 77 77 77 78 80 80 80 78 83 84 84 84 83 83 82 82 82 83
177610 Javi Martinez 26 1988-09-02 190 81 Spain FC Bayern München 84 85 27500000 CB, CDM, CM Right 3 3 2 Medium/Medium Normal Yes SUB 8.0 2012-08-29 2017.0 52.0 61.0 71.0 65.0 84.0 79.0 0.0 Power Header 60 55 86 77 58 61 61 36 76 69 47 53 49 80 59 81 70 52 83 63 88 85 57 75 46 83 86 87 5 12 9 11 8 69 69 69 65 68 68 68 65 69 69 69 67 74 74 74 67 74 81 81 81 74 76 84 84 84 76
1179 G. Buffon 37 1978-01-28 191 83 Italy Juventus 84 84 9000000 GK Right 4 2 1 Medium/Medium Normal Yes GK 1.0 2001-07-01 2017.0 0.0 0.0 0.0 0.0 0.0 0.0 46.0 Leadership, One Club Player, Team Player 1 5 -6 37 9 30 15 1 39 18 53 53 59 78 49 -7 75 39 66 1 42 11 3 75 9 -5 -3 -3 86 81 80 88 86 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
5479 Casillas 34 1981-05-20 185 84 Spain FC Porto 84 84 19000000 GK Left 4 2 1 Medium/Medium Normal Yes GK 12.0 2015-07-12 2017.0 0.0 0.0 0.0 0.0 0.0 0.0 64.0 GK Long Throw, 1-on-1 Rush 1 -1 25 21 3 25 25 1 22 20 65 63 62 80 46 30 77 43 70 1 23 19 7 105 24 -3 -3 1 87 79 67 79 88 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
7763 A. Pirlo 36 1979-05-19 177 68 Italy New York City FC 84 84 2400000 CM, CDM Right 4 4 2 Low/Low Lean Yes CDM 21.0 2015-07-06 2021.0 41.0 69.0 93.0 79.0 52.0 58.0 0.0 Long Passer (CPU AI Only), Playmaker (CPU AI O... 104 56 54 93 76 80 94 93 94 89 45 33 28 88 61 78 33 61 59 82 52 59 48 94 83 40 48 46 5 4 1 5 2 69 69 69 76 76 76 76 76 81 81 81 79 84 84 84 79 70 73 73 73 70 65 59 59 59 65
7826 R. van Persie 31 1983-08-06 187 71 Netherlands Fenerbahçe SK 84 84 25500000 ST Left 4 3 4 Medium/Low Normal Yes LS 11.0 2015-07-14 2018.0 67.0 86.0 81.0 81.0 33.0 67.0 0.0 Injury Prone, Flair 81 85 74 82 92 82 86 81 75 83 63 69 70 84 59 89 59 63 72 82 55 34 85 82 84 23 32 21 9 10 5 7 8 84 84 84 83 84 84 84 83 84 84 84 82 78 78 78 82 61 60 60 60 61 57 51 51 51 57
13743 S. Gerrard 35 1980-05-30 183 83 England LA Galaxy 84 84 10000000 CM, CDM, CAM Right 4 3 3 Medium/Medium Normal Yes LCM 8.0 2015-07-08 2021.0 54.0 82.0 86.0 75.0 69.0 80.0 0.0 Long Passer (CPU AI Only), Long Shot Taker (CP... 86 79 75 86 80 74 84 82 87 82 55 54 59 86 60 90 71 74 82 82 85 70 82 85 85 63 73 68 13 15 13 5 10 81 81 81 79 81 81 81 79 82 82 82 80 84 84 84 80 76 80 80 80 76 75 76 76 76 75
45197 Xabi Alonso 33 1981-11-25 183 80 Spain FC Bayern München 84 84 13000000 CDM, CM Right 4 4 2 Low/Medium Normal Yes CM 14.0 2014-08-29 2016.0 33.0 69.0 88.0 77.0 76.0 70.0 0.0 Long Passer (CPU AI Only), Playmaker (CPU AI O... 79 54 72 93 71 77 82 84 94 84 15 18 52 86 66 81 45 60 73 82 78 87 75 88 80 68 76 68 7 8 10 10 10 73 73 73 75 77 77 77 75 80 80 80 76 86 86 86 76 76 84 84 84 76 74 78 78 78 74
120533 Pepe 32 1983-02-26 188 81 Portugal Real Madrid 84 84 17000000 CB Right 3 3 2 Medium/High Lean Yes RCB 3.0 2007-07-01 2017.0 73.0 51.0 58.0 59.0 86.0 82.0 0.0 Dives Into Tackles (CPU AI Only) 46 46 80 67 23 58 44 47 60 57 67 77 57 75 58 63 74 69 83 56 94 87 40 58 57 86 90 90 8 15 5 9 10 61 61 61 59 60 60 60 59 60 60 60 60 64 64 64 60 74 76 76 76 74 77 84 84 84 77
137186 A. Barzagli 34 1981-05-08 186 79 Italy Juventus 84 84 9000000 CB Right 3 3 2 Low/High Normal Yes SUB 15.0 2011-01-01 2016.0 75.0 36.0 55.0 59.0 88.0 77.0 0.0 Injury Prone, Leadership 42 32 76 65 41 52 50 31 65 65 71 79 63 80 78 51 82 61 82 48 77 89 34 54 50 91 91 91 4 2 4 2 4 55 55 55 55 55 55 55 55 57 57 57 58 63 63 63 58 74 77 77 77 74 77 84 84 84 77
139869 W. Sneijder 31 1984-06-09 170 72 Netherlands Galatasaray SK 84 84 24500000 CAM Right 3 5 3 Medium/Low Normal Yes CAM 10.0 2013-01-22 2016.0 74.0 80.0 86.0 84.0 45.0 61.0 0.0 Long Passer (CPU AI Only), Long Shot Taker (CP... 83 74 45 85 80 84 88 85 87 85 71 75 85 83 85 86 51 54 57 86 68 43 81 83 75 42 50 46 9 5 13 11 12 77 77 77 82 82 82 82 82 84 84 84 82 81 81 81 82 67 67 67 67 67 63 55 55 55 63
146530 Dani Alves 32 1983-05-06 172 70 Brazil FC Barcelona 84 84 16000000 RB Right 4 3 3 High/Low Normal Yes RB 6.0 2008-07-01 2017.0 86.0 70.0 76.0 83.0 78.0 69.0 0.0 Power Free-Kick, Diver 75 60 73 77 68 81 77 74 73 85 84 89 86 79 82 86 78 79 57 77 85 83 63 67 70 74 85 89 5 11 9 6 7 76 76 76 79 78 78 78 79 79 79 79 80 79 79 79 80 84 81 81 81 84 84 80 80 80 84
150724 J. Hart 28 1987-04-19 196 91 England Manchester City 84 84 25000000 GK Right 3 3 1 Medium/Medium Normal Yes GK 1.0 2006-05-24 2019.0 0.0 0.0 0.0 0.0 0.0 0.0 60.0 Injury Free, Leadership, Puncher 3 3 -5 32 13 21 9 1 35 20 59 60 25 81 32 105 72 28 61 13 28 25 3 59 92 3 3 1 85 80 76 88 88 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
168609 Miranda 30 1984-09-07 186 76 Brazil Inter 84 84 21000000 CB Right 3 3 2 Medium/Medium Lean Yes RCB 25.0 2015-07-01 2016.0 76.0 49.0 56.0 57.0 86.0 80.0 0.0 Dives Into Tackles (CPU AI Only) 48 43 86 64 51 49 32 39 65 64 74 77 67 75 58 70 86 71 80 41 88 83 43 53 49 86 89 89 12 6 10 13 12 62 62 62 58 59 59 59 58 59 59 59 60 63 63 63 60 73 76 76 76 73 77 84 84 84 77
173210 C. Marchisio 29 1986-01-19 179 76 Italy Juventus 84 84 26500000 CM, CDM Right 3 4 4 High/High Lean Yes SUB 8.0 2007-06-01 2020.0 81.0 74.0 82.0 83.0 77.0 78.0 0.0 Flair, Long Shot Taker (CPU AI Only), Technica... 76 62 60 85 76 82 84 78 84 85 81 81 83 88 76 80 63 91 69 86 87 83 81 85 80 84 80 81 4 3 4 2 4 78 78 78 82 82 82 82 82 83 83 83 83 84 84 84 83 83 83 83 83 83 82 79 79 79 82
178088 Juan Mata 27 1988-04-28 170 63 Spain Manchester United 84 84 28500000 RM, CAM Left 3 3 4 Medium/Medium Lean Yes RM 8.0 2014-01-25 2018.0 71.0 75.0 84.0 87.0 27.0 55.0 0.0 Finesse Shot, Flair, Playmaker (CPU AI Only), ... 83 77 66 86 71 85 82 81 78 88 77 60 90 87 89 74 72 79 45 72 52 39 84 88 71 5 22 7 9 10 14 5 8 78 78 78 84 83 83 83 84 85 85 85 84 80 80 80 84 61 59 59 59 61 56 44 44 44 56
189362 Hulk 28 1986-07-25 180 85 Brazil Zenit St. Petersburg 84 84 28500000 RW, ST, RM Left 3 3 4 High/Medium Stocky Yes RAM 7.0 2012-09-03 2019.0 85.0 87.0 81.0 82.0 43.0 86.0 0.0 Diver, Flair, Long Shot Taker (CPU AI Only), S... 80 85 68 83 73 84 80 84 78 84 84 85 72 83 59 94 72 84 90 90 84 55 83 84 80 33 41 28 6 10 7 11 12 85 85 85 84 85 85 85 84 84 84 84 84 80 80 80 84 68 68 68 68 68 64 60 60 60 64
192366 N. Otamendi 27 1988-02-12 183 81 Argentina Manchester City 84 84 24000000 CB Right 3 3 2 Medium/Medium Normal No RCB 30.0 2015-08-20 2020.0 75.0 56.0 56.0 54.0 87.0 82.0 0.0 Injury Free, Dives Into Tackles (CPU AI Only),... 50 47 88 59 58 26 50 34 68 57 74 76 71 80 51 69 90 78 79 52 88 86 43 28 44 89 87 91 12 5 8 11 12 65 65 65 59 60 60 60 59 59 59 59 60 64 64 64 60 75 77 77 77 75 79 84 84 84 79
196144 J. Martínez 28 1986-10-03 185 85 Colombia Atlético Madrid 84 84 29500000 ST Right 3 4 4 High/Medium Normal No RS 11.0 2015-07-16 2017.0 81.0 82.0 67.0 79.0 44.0 85.0 0.0 Beat Offside Trap, Power Header 61 89 86 72 83 80 73 46 63 82 76 85 81 84 46 82 85 80 88 70 83 47 86 72 70 27 47 33 15 5 12 14 14 84 84 84 79 82 82 82 79 79 79 79 78 74 74 74 78 63 63 63 63 63 61 61 61 61 61
194765 A. Griezmann 24 1991-03-21 176 67 France Atlético Madrid 83 89 33000000 ST, CAM, LM Left 3 3 4 High/Medium Lean Yes LS 7.0 2014-07-29 2020.0 85.0 81.0 77.0 84.0 29.0 68.0 0.0 Speed Dribbler (CPU AI Only) 82 85 80 77 83 86 84 70 74 83 87 83 89 82 70 81 79 75 62 82 68 35 88 74 64 23 22 -3 14 8 14 13 14 83 83 83 84 83 83 83 84 82 82 82 83 75 75 75 83 60 57 57 57 60 55 48 48 48 55
189509 Thiago 24 1991-04-11 174 70 Spain FC Bayern München 83 87 30000000 CM, CAM, CDM Right 3 3 4 Medium/Medium Normal Yes LDM 6.0 2013-07-14 2019.0 72.0 75.0 81.0 89.0 57.0 64.0 0.0 Injury Prone, Flair, Playmaker (CPU AI Only) 69 69 52 89 95 92 84 77 83 88 77 68 90 82 83 85 72 72 60 86 67 67 83 84 75 49 58 57 6 11 7 9 13 77 77 77 82 82 82 82 82 85 85 85 81 83 83 83 81 71 73 73 73 71 68 64 64 64 68
193747 Koke 23 1992-01-08 178 74 Spain Atlético Madrid 83 87 31000000 CAM, LM, RM, CM Right 3 4 3 High/High Normal Yes LM 6.0 2011-01-01 2019.0 74.0 72.0 85.0 81.0 53.0 73.0 0.0 Playmaker (CPU AI Only) 84 68 62 84 56 79 85 73 87 86 75 72 78 84 79 81 61 78 72 82 68 48 76 88 59 50 51 48 14 12 5 10 13 76 76 76 81 81 81 81 81 83 83 83 82 82 82 82 82 72 72 72 72 72 68 62 62 62 68
212218 A. Laporte 21 1994-05-27 189 85 France Athletic Club de Bilbao 83 87 26000000 CB Left 2 2 2 Medium/Medium Lean Yes LCB 4.0 2012-10-01 2019.0 73.0 37.0 59.0 60.0 86.0 78.0 0.0 NaN 33 27 88 72 32 67 54 76 80 71 77 79 40 84 54 72 70 70 83 29 65 86 36 50 37 92 94 91 10 11 5 14 5 58 58 58 54 57 57 57 54 57 57 57 59 65 65 65 59 72 78 78 78 72 76 83 83 83 76
171833 D. Sturridge 25 1989-09-01 188 76 England Liverpool 83 86 29500000 ST, RW Left 3 2 3 Medium/Low Normal Yes RS 15.0 2013-01-02 2019.0 89.0 83.0 69.0 81.0 25.0 70.0 0.0 Injury Prone, Selfish, Finesse Shot, Flair, Sp... 62 85 73 75 76 84 62 68 62 81 88 88 78 82 64 83 74 72 73 83 60 22 84 74 75 5 24 -1 7 15 9 5 13 83 83 83 82 83 83 83 82 81 81 81 80 72 72 72 80 55 53 53 53 55 52 45 45 45 52
176676 Marcelo 27 1988-05-12 174 75 Brazil Real Madrid 83 86 22500000 LB Left 3 4 3 High/Medium Normal Yes LB 12.0 2007-01-01 2020.0 81.0 68.0 77.0 84.0 81.0 79.0 0.0 NaN 85 68 75 78 54 83 80 67 76 86 77 83 79 85 84 83 75 80 75 65 86 83 67 70 59 84 87 89 12 5 5 5 9 76 76 76 79 78 78 78 79 78 78 78 80 79 79 79 80 84 82 82 82 84 83 82 82 82 83
202857 K. Bellarabi 25 1990-04-08 183 80 Germany Bayer 04 Leverkusen 83 85 27500000 RM, RW, CF Right 3 3 4 High/Low Lean No RM 38.0 2011-07-01 2020.0 90.0 76.0 76.0 85.0 33.0 64.0 0.0 Selfish, Flair, Technical Dribbler (CPU AI Only) 81 77 48 81 74 86 70 58 67 87 91 90 87 81 79 82 75 82 60 79 49 36 83 81 57 28 30 33 10 12 7 11 10 78 78 78 84 82 82 82 84 82 82 82 83 75 75 75 83 64 58 58 58 64 59 48 48 48 59
143745 A. Turan 28 1987-01-30 177 76 Turkey FC Barcelona 83 84 25000000 RM, LM Right 3 4 4 High/High Normal Yes SUB 16.0 2015-07-06 2020.0 75.0 74.0 82.0 87.0 61.0 73.0 0.0 Finesse Shot, Flair, Playmaker (CPU AI Only), ... 84 72 70 82 80 87 81 64 80 90 79 71 86 79 59 74 79 76 69 77 78 58 82 86 73 54 62 66 11 7 11 12 11 79 79 79 83 83 83 83 83 84 84 84 83 82 82 82 83 75 74 74 74 75 73 69 69 69 73
167628 S. Ruffier 28 1986-09-27 188 90 France AS Saint-Étienne 83 84 22500000 GK Right 3 3 1 Medium/Medium Normal No GK 16.0 2011-07-01 2018.0 0.0 0.0 0.0 0.0 0.0 0.0 54.0 NaN -3 -3 -1 33 1 -1 9 -1 34 4 55 54 54 79 55 21 72 37 76 -3 31 25 -1 73 25 -3 -1 -3 84 81 77 83 86 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
170481 E. Garay 28 1986-10-10 188 80 Argentina Zenit St. Petersburg 83 84 21000000 CB Right 3 3 2 Medium/Medium Normal No RCB 24.0 2014-07-01 2019.0 52.0 57.0 68.0 64.0 87.0 78.0 0.0 NaN 62 48 86 72 46 62 55 74 75 73 50 54 57 77 38 76 71 70 85 60 74 92 49 63 66 88 87 84 14 6 13 13 14 65 65 65 62 64 64 64 62 65 65 65 65 71 71 71 65 75 80 80 80 75 77 83 83 83 77
179784 B. Höwedes 27 1988-02-29 187 82 Germany FC Schalke 04 83 84 21500000 CB, RB, LB Right 3 3 2 Medium/Medium Normal Yes SUB 4.0 2007-07-01 2017.0 65.0 47.0 63.0 65.0 85.0 78.0 0.0 Injury Prone, Leadership, Team Player 51 48 85 74 38 60 38 52 64 68 62 66 65 82 62 62 82 69 81 31 78 88 51 57 56 87 85 84 14 6 9 10 7 64 64 64 62 63 63 63 62 63 63 63 65 68 68 68 65 75 78 78 78 75 78 83 83 83 78
179944 David Luiz 28 1987-04-22 189 84 Brazil Paris Saint-Germain 83 84 21000000 CB Right 4 3 3 High/Medium Normal Yes LCB 32.0 2014-07-01 2019.0 73.0 64.0 73.0 73.0 83.0 77.0 0.0 Long Throw-in, Power Free-Kick, Long Passer (C... 68 56 83 78 67 70 70 76 76 79 69 68 73 81 56 74 84 67 77 71 84 80 57 68 75 82 88 86 11 12 10 7 14 72 72 72 72 72 72 72 72 73 73 73 73 76 76 76 73 79 81 81 81 79 80 83 83 83 80
184144 N. Gaitán 27 1988-02-23 174 69 Argentina SL Benfica 83 84 25500000 LM Left 2 2 4 Medium/Medium Normal No LM 10.0 2010-07-01 2018.0 83.0 74.0 84.0 86.0 40.0 57.0 0.0 Avoids Using Weaker Foot, Flair, Playmaker (CP... 86 73 57 86 84 87 89 72 81 85 85 82 88 83 83 75 76 70 50 74 57 40 79 85 71 30 42 43 8 11 9 7 5 76 76 76 83 81 81 81 83 83 83 83 83 78 78 78 83 66 62 62 62 66 62 51 51 51 62
53302 D. De Rossi 31 1983-07-24 185 83 Italy Roma 83 83 15000000 CDM Right 3 4 2 Medium/High Normal Yes CDM 16.0 2002-07-01 2017.0 68.0 66.0 74.0 72.0 83.0 84.0 0.0 Leadership, One Club Player, Team Player 60 53 84 82 74 69 67 71 79 78 69 68 69 80 75 81 78 84 84 75 84 88 59 72 75 75 85 83 10 7 13 10 9 72 72 72 70 72 72 72 70 73 73 73 72 78 78 78 72 79 83 83 83 79 80 83 83 83 80
53612 P. Mertesacker 30 1984-09-29 198 90 Germany Arsenal 83 83 17000000 CB Right 3 3 2 Medium/Medium Normal Yes SUB 4.0 2011-08-31 2017.0 31.0 41.0 57.0 48.0 88.0 75.0 0.0 Injury Free, Leadership, Power Header 38 36 86 71 30 39 39 45 58 65 29 33 29 84 28 71 33 64 88 25 69 90 43 58 42 89 89 88 12 13 5 12 8 57 57 57 49 54 54 54 49 54 54 54 53 64 64 64 53 69 77 77 77 69 72 83 83 83 72
112316 J. Mathieu 31 1983-10-29 189 84 France FC Barcelona 83 83 15000000 CB, LB Left 3 2 2 High/Medium Normal Yes SUB 24.0 2014-07-23 2018.0 76.0 64.0 66.0 55.0 84.0 82.0 0.0 Injury Prone, Avoids Using Weaker Foot, Dives ... 79 52 83 66 68 45 79 70 76 65 70 80 65 76 50 80 75 72 90 77 81 85 48 42 60 85 88 79 13 14 10 9 5 67 67 67 63 62 62 62 63 61 61 61 64 66 66 66 64 76 77 77 77 76 79 83 83 83 79
142754 J. Mascherano 31 1984-06-08 174 73 Argentina FC Barcelona 83 83 15000000 CB, CDM Right 3 3 2 Medium/High Normal Yes LCB 14.0 2010-08-01 2018.0 68.0 59.0 71.0 69.0 85.0 80.0 0.0 Dives Into Tackles (CPU AI Only), Long Passer ... 63 51 70 80 59 58 63 50 75 82 69 67 72 82 79 76 75 87 74 59 89 92 51 68 64 86 87 85 6 10 8 5 5 67 67 67 68 68 68 68 68 70 70 70 70 76 76 76 70 81 84 84 84 81 82 83 83 83 82
162347 João Moutinho 28 1986-09-08 170 61 Portugal AS Monaco 83 83 23000000 CAM, CDM Right 3 4 3 Medium/Medium Normal No CAM 8.0 2013-07-01 2018.0 71.0 76.0 83.0 82.0 71.0 71.0 0.0 Injury Free, Playmaker (CPU AI Only) 78 74 68 86 77 81 82 79 83 84 71 65 80 86 83 78 81 91 56 83 77 75 78 84 70 67 75 68 13 15 15 13 13 77 77 77 81 81 81 81 81 83 83 83 82 84 84 84 82 79 80 80 80 79 77 73 73 73 77
163631 L. Baines 30 1984-12-11 170 70 England Everton 83 83 16500000 LB Left 3 3 3 High/Medium Normal Yes SUB 3.0 2007-08-07 2018.0 75.0 75.0 81.0 77.0 82.0 74.0 0.0 Early Crosser 94 70 74 78 63 77 82 82 74 75 77 73 76 80 85 84 72 87 70 75 74 80 76 77 92 86 86 87 8 15 10 13 12 76 76 76 78 77 77 77 78 78 78 78 79 79 79 79 79 83 81 81 81 83 83 80 80 80 83
163705 S. Mandanda 30 1985-03-28 185 82 France Olympique de Marseille 83 83 19000000 GK Right 3 3 1 Medium/Medium Normal Yes GK 30.0 2007-07-01 2016.0 0.0 0.0 0.0 0.0 0.0 0.0 48.0 Leadership 3 1 1 31 1 -3 1 -1 28 -1 53 43 52 83 36 14 74 31 61 -3 37 23 -3 3 -3 -5 -1 1 87 78 79 82 86 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
164468 G. Cahill 29 1985-12-19 193 86 England Chelsea 83 83 17500000 CB Right 2 3 2 Medium/Medium Normal Yes LCB 24.0 2012-01-16 2017.0 73.0 58.0 52.0 62.0 86.0 76.0 0.0 Power Header 28 56 86 65 47 58 48 27 61 64 69 76 67 84 51 63 83 68 79 66 78 90 45 56 53 86 89 90 5 12 9 5 12 65 65 65 60 62 62 62 60 62 62 62 60 65 65 65 60 71 75 75 75 71 75 83 83 83 75
165229 L. Koscielny 29 1985-09-10 186 75 France Arsenal 83 83 17500000 CB Right 3 3 2 Medium/High Lean Yes LCB 6.0 2010-07-07 2019.0 78.0 40.0 62.0 65.0 84.0 75.0 0.0 Power Header 54 32 81 75 35 62 22 49 67 67 79 74 70 78 62 54 86 73 71 47 88 85 31 56 51 85 89 87 13 11 9 11 7 58 58 58 61 60 60 60 61 62 62 62 64 67 67 67 64 76 78 78 78 76 79 83 83 83 79
165239 S. Nasri 28 1987-06-26 175 75 France Manchester City 83 83 22500000 LM, RM Right 3 4 4 High/Medium Normal Yes SUB 8.0 2011-08-24 2019.0 81.0 76.0 83.0 86.0 38.0 58.0 0.0 Flair, Technical Dribbler (CPU AI Only) 81 76 29 86 73 86 78 79 72 88 86 79 82 77 92 75 50 72 60 77 33 37 81 85 73 35 37 44 -3 -7 -11 -13 -6 77 77 77 84 83 83 83 84 84 84 84 83 79 79 79 83 66 62 62 62 66 61 49 49 49 61
165580 Diego Alves 30 1985-06-24 188 83 Brazil Valencia CF 83 83 19000000 GK Left 3 2 1 Medium/Medium Normal Yes RES 1.0 2011-07-01 2019.0 0.0 0.0 0.0 0.0 0.0 0.0 54.0 NaN 14 20 20 33 14 3 16 21 33 5 55 54 69 80 57 3 84 43 67 12 44 13 1 103 18 3 1 5 88 76 76 77 88 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
167397 Falcao 29 1986-02-10 177 72 Colombia Chelsea 83 83 23000000 ST Right 4 4 4 High/Medium Normal Yes SUB 9.0 2015-07-01 2016.0 72.0 82.0 64.0 79.0 37.0 73.0 0.0 Injury Prone, Finesse Shot, Power Header 55 84 90 69 81 72 83 71 53 83 73 66 85 85 75 79 92 71 73 77 70 41 85 68 81 7 42 13 10 13 6 9 5 83 83 83 78 81 81 81 78 78 78 78 75 72 72 72 75 58 58 58 58 58 56 55 55 55 56
173221 A. Candreva 28 1987-02-28 181 72 Italy Lazio 83 83 23000000 RW Right 2 4 3 High/Medium Lean No RAM 87.0 2012-01-01 2019.0 86.0 81.0 82.0 85.0 45.0 74.0 0.0 Power Free-Kick, Flair, Long Shot Taker (CPU A... 90 78 55 80 79 87 87 79 75 89 88 86 80 85 76 91 57 93 68 88 66 49 77 81 88 41 44 44 10 4 14 4 14 79 79 79 83 82 82 82 83 82 82 82 84 79 79 79 84 71 66 66 66 71 67 56 56 56 67
174543 C. Bravo 32 1983-04-13 184 80 Chile FC Barcelona 83 83 17500000 GK Right 3 3 1 Medium/Medium Normal No GK 13.0 2014-07-01 2018.0 0.0 0.0 0.0 0.0 0.0 0.0 56.0 Puncher -1 1 11 32 -3 1 24 95 31 5 58 54 64 77 64 12 81 44 70 11 40 23 5 101 23 -3 11 13 81 82 89 78 86 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
179846 S. Khedira 28 1987-04-04 189 85 Germany Juventus 83 83 18500000 CDM, CM Right 3 4 2 Medium/High Normal Yes SUB 6.0 2015-07-01 2019.0 62.0 70.0 73.0 70.0 83.0 86.0 0.0 Injury Prone 55 62 72 81 78 76 69 58 75 77 60 75 58 85 51 84 68 82 89 76 88 85 80 77 61 83 86 79 11 9 5 15 8 75 75 75 71 75 75 75 71 75 75 75 73 80 80 80 73 78 83 83 83 78 79 83 83 83 79
184344 L. Bonucci 28 1987-05-01 190 82 Italy Juventus 83 83 18500000 CB Right 2 3 2 Medium/High Lean Yes RCB 19.0 2010-07-01 2020.0 71.0 50.0 66.0 67.0 85.0 81.0 0.0 Power Free-Kick, Injury Free, Leadership, Long... 58 41 84 75 65 63 60 81 75 73 61 77 69 86 57 73 83 75 85 73 89 85 40 63 76 88 93 90 2 2 3 2 4 63 63 63 62 63 63 63 62 65 65 65 64 71 71 71 64 75 80 80 80 75 77 83 83 83 77
185221 Luiz Gustavo 27 1987-07-23 187 80 Brazil VfL Wolfsburg 83 83 19000000 CDM Left 3 3 2 Medium/High Lean Yes CDM 22.0 2013-08-16 2018.0 70.0 60.0 70.0 71.0 84.0 82.0 0.0 Dives Into Tackles (CPU AI Only) 59 47 76 81 50 68 36 39 79 75 69 67 64 83 59 87 76 81 81 75 88 85 51 70 60 81 87 88 10 6 11 12 13 68 68 68 69 69 69 69 69 71 71 71 71 76 76 76 71 80 83 83 83 80 81 83 83 83 81
189505 Pedro 27 1987-07-28 167 62 Spain Chelsea 83 83 24000000 LW, RW Right 3 5 4 High/Medium Normal Yes RM 17.0 2015-08-20 2019.0 82.0 76.0 77.0 84.0 37.0 62.0 0.0 NaN 78 80 55 83 70 84 81 57 74 85 86 78 84 85 82 71 67 78 58 73 56 49 84 74 66 30 32 31 5 11 12 15 9 78 78 78 83 82 82 82 83 82 82 82 83 77 77 77 83 66 62 62 62 66 61 50 50 50 61
201535 R. Varane 22 1993-04-25 191 78 France Real Madrid 82 89 24500000 CB Right 3 3 2 Medium/Medium Lean Yes SUB 2.0 2011-07-01 2020.0 79.0 45.0 60.0 63.0 84.0 77.0 0.0 NaN 36 40 82 71 42 57 46 54 74 74 77 79 56 78 47 52 72 68 84 53 73 85 44 56 43 86 86 85 11 11 9 5 14 62 62 62 61 63 63 63 61 64 64 64 64 69 69 69 64 74 79 79 79 74 77 82 82 82 77
192448 M. ter Stegen 23 1992-04-30 187 85 Germany FC Barcelona 82 88 23500000 GK Right 2 4 1 Medium/Medium Normal Yes SUB 1.0 2014-07-01 2019.0 0.0 0.0 0.0 0.0 0.0 0.0 45.0 NaN 5 3 -5 30 3 9 11 -1 38 8 38 50 37 79 43 6 82 35 79 -5 43 22 -3 89 17 -5 1 -5 85 79 82 78 85 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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230133 F. Moncur 18 1996-09-08 175 68 England Leyton Orient 49 60 50000 CM Right 1 3 3 Medium/Medium Normal No SUB 23.0 2015-07-08 2016.0 68.0 42.0 53.0 50.0 36.0 45.0 0.0 NaN 41 42 37 62 36 48 41 42 53 48 69 67 56 47 76 45 52 48 44 39 41 23 41 53 40 37 40 49 11 7 6 8 6 46 46 46 50 49 49 49 50 51 51 51 51 49 49 49 51 46 45 45 45 46 46 41 41 41 46
222007 C. O'Malley 20 1994-08-01 182 74 Republic of Ireland St. Patrick's Athletic 49 59 50000 GK Right 1 2 1 Medium/Medium Lean No SUB 16.0 2015-01-01 2015.0 0.0 0.0 0.0 0.0 0.0 0.0 40.0 NaN 22 5 1 22 13 1 5 11 13 9 38 41 45 39 51 13 52 33 40 -1 11 27 13 1 25 13 -1 7 54 48 42 47 57 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
225866 M. Agyemang 18 1996-11-22 175 67 England Leyton Orient 49 59 40000 RM, RWB Right 1 3 3 Medium/Medium Normal No RES 24.0 2014-10-21 2016.0 71.0 44.0 44.0 50.0 25.0 43.0 0.0 NaN 45 41 37 49 43 48 39 33 38 50 68 74 57 32 76 53 42 51 43 39 35 20 49 42 45 28 22 26 10 6 12 15 15 47 47 47 49 48 48 48 49 47 47 47 49 42 42 42 49 40 35 35 35 40 38 32 32 32 38
227929 R. Wintle 17 1997-12-17 165 64 England Crewe Alexandra 49 59 50000 ST Right 1 2 2 Medium/Medium Lean No RES 21.0 2015-02-02 2016.0 62.0 47.0 46.0 57.0 35.0 41.0 0.0 NaN 44 53 36 49 37 55 39 43 45 55 61 62 64 45 90 51 56 53 34 32 42 23 51 47 43 32 41 44 7 16 8 13 10 49 49 49 52 51 51 51 52 51 51 51 52 47 47 47 52 45 42 42 42 45 44 39 39 39 44
228888 G. Edmundson 17 1997-08-15 185 75 England Oldham Athletic 49 59 40000 CB Right 1 3 2 Medium/Medium Normal No RES 27.0 2015-05-14 2016.0 60.0 25.0 31.0 34.0 49.0 56.0 0.0 NaN 29 20 47 36 25 27 26 29 30 33 62 59 55 47 58 36 70 57 59 19 44 47 29 26 39 43 54 54 14 9 8 14 15 36 36 36 35 34 34 34 35 33 33 33 36 35 35 35 36 45 43 43 43 45 47 49 49 49 47
225867 V. Adeboyejo 17 1998-01-12 178 63 England Leyton Orient 49 58 40000 ST Right 1 3 2 Medium/Medium Lean No RES 29.0 2014-10-21 2016.0 71.0 51.0 41.0 48.0 22.0 40.0 0.0 NaN 33 55 50 45 34 45 32 34 34 42 68 73 64 50 81 47 52 56 34 47 34 15 47 50 58 20 19 18 12 9 13 11 10 49 49 49 49 50 50 50 49 48 48 48 48 42 42 42 48 37 33 33 33 37 36 30 30 30 36
228331 J. Jewson 17 1998-03-03 178 78 England Hartlepool United 49 58 40000 ST, CAM, CM Right 1 3 3 High/Medium Lean No RES 35.0 2014-12-15 2016.0 63.0 49.0 41.0 56.0 18.0 52.0 0.0 NaN 35 47 42 42 47 60 48 29 32 51 65 61 57 52 66 62 52 58 59 47 27 7 38 56 60 1 9 -1 16 9 11 13 17 49 49 49 50 50 50 50 50 49 49 49 49 43 43 43 49 36 33 33 33 36 34 29 29 29 34
229979 R. Vergara 20 1995-06-13 177 69 Colombia Boyacá Chicó FC 49 57 50000 ST Right 1 2 3 Low/Low Lean No RES 24.0 2015-07-16 2021.0 68.0 49.0 36.0 47.0 18.0 55.0 0.0 NaN 26 48 48 41 33 44 33 28 31 42 69 67 71 54 70 52 64 77 72 50 26 7 43 45 63 -3 7 11 6 5 11 6 11 49 49 49 47 48 48 48 47 46 46 46 46 41 41 41 46 36 32 32 32 36 35 30 30 30 35
230806 M. Carberry 17 1998-03-05 170 63 England Doncaster Rovers 49 57 30000 LB, LM Left 1 2 2 High/Medium Normal No RES 33.0 2015-07-01 2018.0 61.0 26.0 37.0 43.0 46.0 43.0 0.0 NaN 45 20 34 36 28 40 36 27 34 37 63 60 54 53 75 32 50 57 36 27 40 47 54 34 29 40 54 52 11 6 8 8 13 38 38 38 42 41 41 41 42 40 40 40 43 40 40 40 43 48 43 43 43 48 49 45 45 45 49
224392 T. McCready 24 1991-06-07 185 71 England Exeter City 49 56 50000 CM Right 1 3 2 Medium/Medium Normal No SUB 23.0 2015-02-02 2021.0 64.0 42.0 50.0 54.0 35.0 49.0 0.0 NaN 43 42 42 57 42 55 43 39 56 52 62 65 64 48 60 51 56 49 51 38 42 36 45 48 47 34 37 35 9 11 12 11 17 48 48 48 50 50 50 50 50 51 51 51 51 49 49 49 51 45 45 45 45 45 44 41 41 41 44
224502 B. Whitehouse 19 1996-06-13 180 72 England Doncaster Rovers 49 56 40000 RM Right 1 2 2 Medium/Medium Lean No RES 32.0 2014-07-01 2017.0 62.0 45.0 46.0 52.0 25.0 49.0 0.0 NaN 48 46 32 55 45 59 45 33 47 47 60 68 58 34 73 54 49 50 62 35 31 24 48 45 54 22 31 31 11 13 12 9 7 46 46 46 48 47 47 47 48 47 47 47 49 43 43 43 49 40 37 37 37 40 38 33 33 33 38
226145 N. Bondswell 18 1997-02-10 173 64 England Morecambe 49 56 40000 LM, LB Left 1 3 3 Medium/Medium Lean No RES 14.0 2014-11-20 2021.0 72.0 40.0 44.0 53.0 27.0 38.0 0.0 NaN 45 39 37 48 40 53 45 37 41 50 71 73 71 40 65 49 42 54 27 38 31 23 42 43 48 26 29 29 17 15 14 10 10 45 45 45 49 47 47 47 49 47 47 47 49 43 43 43 49 41 36 36 36 41 40 32 32 32 40
226821 C. Brandt 22 1992-09-15 185 75 United States New York City FC 49 56 50000 CM Right 1 2 2 Medium/Medium Lean No RES 16.0 2015-01-15 2021.0 61.0 36.0 51.0 49.0 39.0 57.0 0.0 NaN 45 29 43 61 35 51 40 38 52 43 68 55 57 52 58 49 64 55 57 41 58 39 49 51 41 41 37 43 8 11 14 8 15 45 45 45 48 48 48 48 48 49 49 49 50 49 49 49 50 47 47 47 47 47 46 45 45 45 46
222742 D. Thomas 19 1995-11-23 185 70 England Bristol Rovers 49 55 40000 CM Right 1 3 2 Medium/Low Lean No RES 18.0 2012-07-01 2021.0 77.0 42.0 49.0 58.0 34.0 49.0 0.0 NaN 39 39 37 57 40 56 47 36 51 55 78 76 75 47 70 53 55 55 47 36 45 29 47 46 47 34 35 41 15 15 9 11 12 49 49 49 53 52 52 52 53 53 53 53 54 49 49 49 54 47 45 45 45 47 46 41 41 41 46
225772 K. Green 18 1997-06-30 175 80 England Hartlepool United 49 55 40000 CM Right 1 3 2 Medium/Medium Lean No RES 24.0 2014-10-01 2016.0 62.0 35.0 49.0 47.0 41.0 58.0 0.0 NaN 40 28 42 60 36 42 37 34 56 51 64 61 58 48 69 50 59 55 61 33 54 43 51 44 45 41 42 47 11 10 10 9 10 45 45 45 47 46 46 46 47 48 48 48 49 49 49 49 49 48 49 49 49 48 48 47 47 47 48
226053 K. Steenson 18 1996-11-06 180 67 England Accrington Stanley 49 55 40000 CM Right 1 3 2 Medium/Medium Lean No RES 23.0 2015-04-08 2016.0 65.0 36.0 48.0 51.0 38.0 51.0 0.0 NaN 41 28 42 60 39 51 34 33 49 49 68 62 59 48 70 50 63 58 44 41 53 38 51 45 38 34 39 50 11 7 12 13 15 46 46 46 49 48 48 48 49 50 50 50 50 49 49 49 50 48 46 46 46 48 47 44 44 44 47
211732 T. Thiele 23 1991-07-31 188 75 Germany Burton Albion 49 53 50000 ST Right 1 3 2 Medium/Medium Lean No SUB 20.0 2015-07-01 2016.0 58.0 49.0 35.0 48.0 21.0 51.0 0.0 NaN 31 50 58 36 40 48 34 32 31 42 61 55 58 47 59 48 64 57 55 49 30 20 48 42 62 17 16 17 15 8 15 13 11 49 49 49 46 47 47 47 46 45 45 45 45 40 40 40 45 35 32 32 32 35 34 32 32 32 34
221498 H. Al Mansour 22 1993-05-19 180 70 Saudi Arabia Najran SC 49 53 40000 CM Right 1 3 2 Medium/Medium Lean No SUB 20.0 2014-01-03 2021.0 66.0 39.0 49.0 47.0 40.0 62.0 0.0 NaN 38 35 51 60 38 48 34 37 52 43 65 67 45 56 66 54 61 57 66 33 59 38 51 51 46 40 40 45 8 11 8 11 13 49 49 49 48 49 49 49 48 49 49 49 50 49 49 49 50 47 48 48 48 47 47 48 48 48 47
229994 J. Castillo 29 1986-02-20 181 80 Colombia Cortuluá 49 49 30000 CDM Right 1 3 2 Medium/Medium Stocky No SUB 17.0 2015-07-21 2021.0 56.0 34.0 45.0 47.0 47.0 61.0 0.0 NaN 42 27 45 53 32 42 30 31 53 50 57 56 55 48 60 45 56 51 69 34 56 40 31 35 48 46 50 53 11 14 5 7 13 43 43 43 44 42 42 42 44 44 44 44 45 46 46 46 45 48 49 49 49 48 49 51 51 51 49
228402 T. Campbell 18 1997-01-10 171 68 England West Bromwich Albion 48 67 70000 ST Right 1 3 3 Medium/Medium Lean No RES 41.0 2015-07-01 2021.0 57.0 50.0 41.0 54.0 18.0 45.0 0.0 NaN 34 50 42 44 49 58 49 26 34 44 60 55 55 46 81 50 59 58 43 51 31 12 45 49 61 20 15 12 10 12 14 14 11 48 48 48 49 49 49 49 49 48 48 48 48 43 43 43 48 35 32 32 32 35 32 28 28 28 32
229589 P. Denton 18 1996-12-22 191 77 England Hartlepool United 48 66 60000 GK Right 1 2 1 Medium/Medium Lean No SUB 31.0 2015-07-13 2016.0 0.0 0.0 0.0 0.0 0.0 0.0 45.0 NaN 16 14 16 23 14 10 15 11 19 15 49 41 31 48 49 23 59 43 69 11 21 22 12 14 22 19 14 10 52 43 47 43 52 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
230734 G. Doré 18 1997-06-03 180 75 France Valenciennes FC 48 66 60000 CM Right 1 3 2 Medium/Medium Normal No RES 34.0 2015-08-17 2018.0 57.0 34.0 48.0 50.0 45.0 53.0 0.0 NaN 39 28 44 55 30 47 38 35 53 51 60 55 56 53 65 50 60 46 55 32 56 49 32 48 44 41 42 54 11 8 9 15 5 43 43 43 46 45 45 45 46 47 47 47 47 48 48 48 47 49 50 50 50 49 49 49 49 49 49
222479 J. Bonilla 19 1996-04-16 183 77 Colombia Uniautónoma FC 48 63 60000 GK Right 1 3 1 Medium/Medium Normal No SUB 12.0 2014-02-01 2021.0 0.0 0.0 0.0 0.0 0.0 0.0 46.0 NaN 7 5 1 34 -1 9 7 9 25 19 48 44 42 48 45 17 56 30 48 15 23 24 5 17 23 11 3 9 52 48 43 46 52 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
227829 B. Murphy 19 1995-12-22 176 75 England Bristol City 48 63 70000 CM Left 1 2 2 Medium/Medium Lean No RES 26.0 2014-07-08 2016.0 65.0 37.0 48.0 47.0 41.0 56.0 0.0 NaN 35 28 47 57 31 45 36 36 51 44 64 65 57 47 74 54 58 56 58 40 51 44 46 47 44 37 40 46 7 14 9 16 15 46 46 46 46 47 47 47 46 48 48 48 48 48 48 48 48 47 47 47 47 47 47 46 46 46 47
228584 A. May 17 1997-12-06 183 73 England Portsmouth 48 62 60000 CM, CAM Right 1 2 2 Medium/Medium Lean No RES 30.0 2014-07-04 2016.0 71.0 43.0 45.0 50.0 46.0 53.0 0.0 NaN 36 44 37 55 38 46 35 33 52 50 70 72 64 52 74 47 68 55 52 41 49 43 52 41 37 50 50 46 8 8 15 14 15 48 48 48 50 49 49 49 50 50 50 50 50 48 48 48 50 49 49 49 49 49 49 48 48 48 49
230722 S. Boyd 17 1998-06-20 191 75 Republic of Ireland Shamrock Rovers 48 62 70000 ST Right 1 3 2 Medium/Medium Lean No RES 18.0 2015-02-01 2016.0 69.0 46.0 39.0 46.0 22.0 57.0 0.0 NaN 46 41 38 40 42 44 49 50 42 41 81 88 49 36 67 48 48 72 55 45 39 27 40 39 33 9 18 6 8 10 5 10 6 48 48 48 47 47 47 47 47 45 45 45 47 41 41 41 47 38 35 35 35 38 36 33 33 33 36
213687 L. Grimshaw 20 1995-02-02 178 75 England Motherwell 48 61 60000 CDM, CB Right 1 2 2 Medium/Medium Normal No LCM 4.0 2015-07-01 2016.0 58.0 36.0 43.0 45.0 44.0 59.0 0.0 NaN 33 32 42 50 27 41 33 37 42 44 63 54 58 54 65 49 63 55 59 33 65 41 36 43 44 41 47 49 13 8 15 12 11 43 43 43 44 44 44 44 44 44 44 44 45 45 45 45 45 47 48 48 48 47 47 49 49 49 47
225427 S. Arango 19 1996-01-27 180 70 Colombia Boyacá Chicó FC 48 61 50000 GK Right 1 3 1 Medium/Medium Normal No SUB 28.0 2014-09-01 2021.0 0.0 0.0 0.0 0.0 0.0 0.0 47.0 NaN 3 11 1 24 7 9 3 -3 23 22 48 46 38 54 60 23 58 45 48 9 13 11 -1 3 23 9 11 -1 56 45 50 44 50 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
229869 L. Roa 19 1996-06-18 170 65 Chile CD Universidad de Concepción 48 61 60000 CM Right 1 2 2 Medium/Medium Lean No RES 30.0 2015-06-02 2021.0 61.0 38.0 55.0 53.0 45.0 43.0 0.0 NaN 61 31 38 61 32 48 38 51 49 52 63 59 68 49 85 51 59 33 38 45 62 45 29 46 38 39 51 50 11 10 8 13 14 43 43 43 50 46 46 46 50 49 49 49 50 48 48 48 50 50 49 49 49 50 50 47 47 47 50
228222 A. Ovalle 18 1997-04-28 175 70 Chile Real Salt Lake 48 60 50000 CM Right 1 2 2 Medium/Medium Lean No RES 26.0 2015-03-06 2021.0 62.0 36.0 46.0 49.0 40.0 53.0 0.0 NaN 39 28 42 56 41 45 35 35 48 50 55 67 56 56 75 52 63 62 47 36 53 43 48 46 41 40 38 47 8 10 13 13 16 45 45 45 47 47 47 47 47 48 48 48 49 48 48 48 49 48 47 47 47 48 48 45 45 45 48
221809 G. Boylan 19 1996-04-24 179 72 Republic of Ireland Sligo Rovers 48 59 50000 CB Right 1 3 2 Medium/Medium Normal No SUB 14.0 2013-12-13 2015.0 59.0 26.0 27.0 33.0 48.0 59.0 0.0 NaN 23 15 44 28 22 31 27 28 24 32 63 53 32 42 72 38 88 80 56 25 51 51 31 36 36 48 54 56 16 15 11 11 15 35 35 35 33 33 33 33 33 32 32 32 35 34 34 34 35 44 42 42 42 44 46 48 48 48 46
222072 H. Hickford 19 1996-06-23 183 70 England Milton Keynes Dons 48 59 50000 CB, RB Right 1 2 2 Medium/Medium Lean No RES 25.0 2014-02-18 2017.0 60.0 23.0 31.0 31.0 49.0 52.0 0.0 NaN 26 17 44 35 28 26 25 27 29 27 59 60 40 49 64 38 70 57 51 18 43 52 27 31 32 44 53 52 13 14 9 6 7 34 34 34 33 33 33 33 33 32 32 32 35 35 35 35 35 45 43 43 43 45 47 48 48 48 47
223091 R. Donelon 19 1996-04-17 171 71 England Sligo Rovers 48 59 50000 CB Right 1 2 2 Medium/Medium Lean No LB 19.0 2013-08-01 2015.0 53.0 24.0 34.0 32.0 49.0 55.0 0.0 NaN 26 9 60 41 13 30 29 25 37 30 52 54 35 43 59 40 54 57 57 22 50 46 29 35 36 52 53 50 15 15 13 10 15 35 35 35 33 33 33 33 33 33 33 33 35 36 36 36 35 43 43 43 43 43 45 48 48 48 45
223974 J. Bayly 19 1996-06-18 165 65 Republic of Ireland St. Patrick's Athletic 48 59 50000 CM Right 1 2 2 Medium/Medium Lean No SUB 26.0 2013-08-01 2015.0 72.0 39.0 46.0 53.0 43.0 44.0 0.0 NaN 39 37 47 52 40 48 42 37 48 50 77 77 85 47 83 46 55 59 32 40 54 38 50 51 41 44 46 42 10 9 8 10 9 46 46 46 50 49 49 49 50 50 50 50 50 48 48 48 50 48 46 46 46 48 48 44 44 44 48
225639 L. Ranelli 19 1996-06-05 178 69 Italy Frosinone 48 59 50000 CM Left 1 3 2 Medium/Medium Normal No RES 44.0 2014-12-29 2016.0 58.0 36.0 50.0 48.0 40.0 49.0 0.0 NaN 52 28 58 59 40 46 36 36 51 46 62 47 55 55 84 56 63 63 39 35 55 38 51 43 42 34 40 50 15 15 9 14 11 46 46 46 47 47 47 47 47 47 47 47 50 48 48 48 50 49 46 46 46 49 49 45 45 45 49
226018 K. Tshimanga 17 1997-07-22 180 78 England Milton Keynes Dons 48 59 50000 ST Right 1 3 2 Medium/Medium Lean No RES 27.0 2014-11-08 2016.0 61.0 48.0 35.0 48.0 19.0 52.0 0.0 NaN 27 51 46 39 40 49 41 32 27 42 64 58 57 48 65 41 55 56 56 52 37 15 51 45 46 3 5 7 8 14 17 10 12 48 48 48 47 48 48 48 47 46 46 46 45 40 40 40 45 34 31 31 31 34 33 30 30 30 33
229420 C. Hall 18 1997-03-23 180 75 England Plymouth Argyle 48 59 50000 LM Left 1 2 3 High/High Lean No RES 22.0 2015-07-01 2016.0 63.0 38.0 46.0 47.0 40.0 56.0 0.0 NaN 49 32 41 51 37 49 42 38 42 37 62 64 55 45 65 50 61 65 55 37 46 38 51 42 42 35 44 44 11 5 13 9 14 45 45 45 47 46 46 46 47 46 46 46 48 45 45 45 48 48 44 44 44 48 47 44 44 44 47
219509 A. Smith 22 1993-05-25 185 78 Republic of Ireland Cork City 48 58 50000 GK Right 1 1 1 Medium/Medium Normal No SUB 16.0 2015-02-17 2015.0 0.0 0.0 0.0 0.0 0.0 0.0 45.0 NaN 13 15 3 29 5 9 11 -3 23 13 49 41 34 49 56 22 56 21 62 -3 9 7 -3 1 15 15 11 11 56 45 44 42 55 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
222317 F. Reilly 18 1996-07-06 176 68 Republic of Ireland Drogheda United 48 58 40000 LM Left 1 3 2 Medium/Medium Normal No SUB 26.0 2013-01-01 2015.0 64.0 44.0 44.0 49.0 27.0 38.0 0.0 NaN 45 45 37 48 34 49 39 33 43 46 63 64 53 37 71 51 31 55 33 36 32 30 44 39 50 22 28 24 10 11 12 15 11 45 45 45 47 46 46 46 47 46 46 46 48 43 43 43 48 41 37 37 37 41 39 32 32 32 39
223905 P. Ng 19 1996-04-27 180 77 England Crewe Alexandra 48 58 40000 CB Right 1 3 2 Medium/Medium Normal No RES 23.0 2014-07-01 2021.0 60.0 22.0 27.0 29.0 48.0 58.0 0.0 NaN 21 16 48 31 29 24 24 30 28 27 60 60 36 45 57 36 67 57 62 18 46 48 24 27 34 43 51 52 10 6 8 16 7 34 34 34 30 31 31 31 30 30 30 30 33 32 32 32 33 43 42 42 42 43 45 48 48 48 45
219676 R. Lane 18 1996-10-30 178 72 England Plymouth Argyle 48 57 40000 CM, LM Left 1 3 2 Medium/Medium Lean No RES 25.0 2013-08-01 2021.0 76.0 52.0 50.0 45.0 47.0 53.0 0.0 NaN 40 48 41 57 54 41 59 64 50 40 70 80 71 49 72 53 74 53 58 63 35 48 45 46 57 48 50 46 17 10 16 12 10 49 49 49 49 49 49 49 49 49 49 49 49 48 48 48 49 50 48 48 48 50 50 48 48 48 50
225922 M. Shelton 18 1996-09-12 183 70 England Burton Albion 48 57 40000 RB Right 1 3 2 Medium/Medium Stocky No RES 26.0 2014-10-23 2016.0 64.0 27.0 32.0 47.0 47.0 56.0 0.0 NaN 41 22 44 26 26 48 32 27 30 39 67 62 59 48 64 30 59 60 57 30 48 47 42 34 39 41 53 50 10 6 11 10 13 39 39 39 42 40 40 40 42 39 39 39 43 38 38 38 43 47 43 43 43 47 48 48 48 48 48
228581 B. Kelly 17 1997-07-26 183 82 Republic of Ireland Dundalk 48 57 40000 GK Right 1 1 1 Medium/Medium Normal No SUB 30.0 2014-10-08 2016.0 0.0 0.0 0.0 0.0 0.0 0.0 45.0 NaN 11 5 7 35 5 9 11 9 29 7 42 47 36 47 56 15 58 15 67 -1 36 22 13 31 4 -1 -3 13 53 47 48 46 52 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
228293 K. Bryce 22 1993-04-16 183 84 United States Chicago Fire 48 56 40000 CDM Right 1 2 2 Medium/High Lean No RES 27.0 2015-03-24 2021.0 55.0 34.0 45.0 48.0 45.0 57.0 0.0 NaN 37 32 51 55 33 47 34 37 52 51 55 55 43 51 55 43 62 34 71 35 52 49 32 38 39 44 45 48 12 14 11 7 14 44 44 44 44 44 44 44 44 45 45 45 45 45 45 45 45 46 48 48 48 46 46 50 50 50 46
228610 O. Al Mazial 22 1992-07-25 170 65 Saudi Arabia Al Shabab 48 55 50000 CM, RB Right 1 2 3 Medium/Medium Lean No RES 4.0 2012-02-06 2017.0 74.0 33.0 50.0 53.0 44.0 48.0 0.0 NaN 51 27 46 58 30 50 32 41 49 51 73 75 68 49 70 46 58 60 39 34 55 44 33 47 44 41 49 50 11 13 12 9 7 43 43 43 49 46 46 46 49 48 48 48 51 48 48 48 51 52 49 49 49 52 52 47 47 47 52
228754 J. Rivers 21 1993-09-10 168 76 England Blackpool 48 55 50000 RM Right 1 2 2 Medium/Medium Lean No SUB 7.0 2015-06-01 2016.0 60.0 43.0 45.0 51.0 29.0 49.0 0.0 NaN 45 39 42 42 49 49 44 43 40 47 61 60 58 43 80 51 42 50 55 42 35 18 47 57 48 28 29 37 14 11 14 11 8 47 47 47 48 48 48 48 48 48 48 48 48 44 44 44 48 40 37 37 37 40 39 36 36 36 39
230014 E. Iandolo 17 1997-08-05 178 71 England Swindon Town 48 55 40000 ST Right 1 2 3 Medium/Medium Lean No RES 28.0 2015-08-04 2016.0 62.0 49.0 34.0 46.0 17.0 48.0 0.0 NaN 26 52 49 38 34 41 37 32 25 45 64 60 59 49 66 49 64 51 54 47 26 18 44 41 57 13 11 14 9 6 10 14 9 48 48 48 45 47 47 47 45 44 44 44 44 38 38 38 44 33 30 30 30 33 32 29 29 29 32
200633 R. McDonald 23 1992-04-11 191 82 England Northampton Town 48 54 40000 CB Left 1 2 2 Medium/Medium Normal No SUB 18.0 2015-07-28 2016.0 61.0 26.0 30.0 28.0 44.0 65.0 0.0 NaN 25 20 54 33 23 22 13 25 31 25 59 62 46 41 57 35 72 62 69 29 59 40 23 35 29 42 46 44 11 13 6 16 7 35 35 35 32 32 32 32 32 31 31 31 34 33 33 33 34 41 41 41 41 41 43 48 48 48 43
225087 F. Al Sagour 19 1996-04-23 175 64 Saudi Arabia Najran SC 48 54 40000 LM Left 1 3 3 Medium/Medium Lean No RES 24.0 2014-01-01 2021.0 62.0 40.0 43.0 54.0 27.0 39.0 0.0 NaN 49 38 32 39 45 56 45 37 41 50 62 62 57 45 67 53 42 46 37 32 36 7 41 49 49 30 30 34 8 8 13 12 15 44 44 44 48 46 46 46 48 46 46 46 48 42 42 42 48 40 35 35 35 40 38 32 32 32 38
225342 J. Stevens 17 1997-08-02 188 77 England Oxford United 48 54 30000 GK Right 1 3 1 Medium/Medium Lean No RES 35.0 2014-07-01 2021.0 0.0 0.0 0.0 0.0 0.0 0.0 43.0 NaN 9 15 3 26 -1 -3 11 -3 26 15 40 45 36 48 53 25 53 7 61 3 24 13 13 25 24 7 -1 7 56 49 44 40 54 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
230779 A. Fox 22 1993-01-15 183 64 England Peterborough United 48 54 50000 LM, LB, LWB Left 1 2 2 High/Medium Normal No RES 18.0 2015-08-01 2017.0 57.0 39.0 50.0 46.0 38.0 49.0 0.0 NaN 53 31 45 55 37 47 41 35 54 38 55 58 51 49 67 53 59 55 43 38 52 40 52 40 43 36 35 44 5 12 7 14 10 45 45 45 47 46 46 46 47 46 46 46 48 46 46 46 48 47 45 45 45 47 46 43 43 43 46
230804 M. Al Sudani 25 1989-10-28 170 70 Saudi Arabia Al Faisaly 48 54 40000 CM Right 1 3 3 Medium/Medium Normal No SUB 22.0 2015-06-17 2018.0 61.0 36.0 50.0 45.0 36.0 58.0 0.0 NaN 41 27 45 60 34 41 35 35 54 43 51 69 53 47 81 51 55 62 57 36 57 36 49 47 42 30 35 49 6 11 14 8 13 45 45 45 46 46 46 46 46 47 47 47 48 48 48 48 48 47 47 47 47 47 46 44 44 44 46
230126 J. Brophy 20 1994-07-25 180 68 England Swindon Town 48 53 40000 LB, LM Left 1 2 2 Medium/Medium Normal No SUB 15.0 2015-08-06 2015.0 64.0 26.0 37.0 50.0 46.0 54.0 0.0 NaN 46 28 42 34 19 51 36 33 33 41 68 61 60 46 69 26 63 54 54 20 53 44 37 36 31 43 51 51 8 10 9 7 6 39 39 39 44 41 41 41 44 40 40 40 44 39 39 39 44 48 44 44 44 48 48 48 48 48 48
228683 F. Almoqati 24 1990-08-10 170 69 Saudi Arabia Al Qadisiyah 48 52 40000 CDM Right 1 3 3 Medium/Medium Lean No SUB 27.0 2013-01-01 2021.0 59.0 25.0 37.0 48.0 53.0 65.0 0.0 NaN 44 21 42 37 23 49 32 32 27 42 64 55 50 47 77 28 62 70 70 28 47 54 41 39 31 46 61 61 12 13 7 7 7 39 39 39 43 41 41 41 43 41 41 41 44 42 42 42 44 52 48 48 48 52 53 53 53 53 53
222012 F. Al Sobaie 27 1988-01-01 168 68 Saudi Arabia Najran SC 48 50 30000 RB, CAM, RM Right 1 2 2 Medium/Medium Normal No SUB 15.0 2014-07-08 2021.0 56.0 28.0 29.0 45.0 54.0 56.0 0.0 NaN 34 27 44 21 28 46 26 28 30 34 55 56 60 46 79 25 65 53 58 30 52 49 38 40 35 53 59 61 9 11 12 9 7 37 37 37 39 38 38 38 39 37 37 37 39 37 37 37 39 46 44 44 44 46 48 52 52 52 48
228676 J. Dyche 17 1997-10-11 175 65 England Scunthorpe United 47 68 70000 CAM, LB Left 1 3 2 Medium/Medium Lean No RES 24.0 2015-03-05 2016.0 63.0 44.0 41.0 52.0 44.0 56.0 0.0 NaN 41 48 45 44 22 55 31 29 42 42 61 64 59 46 78 38 62 59 56 47 51 47 49 39 43 37 46 50 13 9 15 11 9 48 48 48 49 48 48 48 49 47 47 47 49 45 45 45 49 48 46 46 46 48 48 47 47 47 48
227529 M. Hudson 16 1998-07-29 184 70 England Preston North End 47 65 60000 GK Right 1 3 1 Medium/Medium Lean No RES 31.0 2015-02-03 2016.0 0.0 0.0 0.0 0.0 0.0 0.0 45.0 NaN 19 18 11 29 13 12 20 11 16 20 49 42 37 49 54 20 51 35 45 19 17 24 19 15 17 16 13 20 46 46 42 45 53 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
230609 S. Fogarty 17 1997-08-16 183 80 Republic of Ireland Bray Wanderers 47 64 60000 GK Right 1 1 1 Medium/Medium Lean No SUB 50.0 2015-07-10 2016.0 0.0 0.0 0.0 0.0 0.0 0.0 46.0 NaN 7 -3 -5 23 -1 5 9 9 7 13 42 51 40 51 73 11 72 9 54 5 22 11 -3 29 0 1 -3 13 46 43 51 49 49 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
227931 J. Storer 17 1998-01-02 185 70 England Stevenage 47 61 60000 CM Right 1 3 2 Medium/Medium Normal No RES 38.0 2015-01-01 2016.0 67.0 36.0 45.0 47.0 42.0 58.0 0.0 NaN 33 29 48 56 35 48 35 32 49 43 65 68 53 44 61 51 55 62 57 31 58 45 45 42 42 37 43 46 14 7 12 11 9 45 45 45 46 46 46 46 46 46 46 46 48 47 47 47 48 48 48 48 48 48 48 47 47 47 48
229560 C. Chambers 17 1998-01-11 183 74 Republic of Ireland Bray Wanderers 47 61 60000 GK Right 1 1 1 Medium/Medium Lean No RES 51.0 2015-07-02 2016.0 0.0 0.0 0.0 0.0 0.0 0.0 41.0 NaN 11 -3 13 24 9 5 7 13 21 23 41 41 39 47 61 13 56 24 55 5 13 20 -5 30 4 5 1 13 50 45 48 46 49 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
229669 Matheus Silva 18 1996-12-08 188 90 Brazil San Jose Earthquakes 47 61 60000 CDM, CB Right 1 3 2 Medium/High Lean No RES 49.0 2015-07-15 2021.0 55.0 32.0 42.0 43.0 47.0 63.0 0.0 NaN 38 29 42 46 29 43 30 34 45 41 55 55 41 50 45 40 61 34 83 31 50 49 31 42 35 43 49 49 10 13 9 12 7 41 41 41 42 41 41 41 42 42 42 42 42 43 43 43 42 45 47 47 47 45 46 51 51 51 46
229850 C. Toonga 17 1997-11-20 175 76 England AFC Wimbledon 47 61 60000 CM, LM Left 1 2 2 Medium/Medium Normal No RES 28.0 2015-06-12 2016.0 71.0 39.0 44.0 52.0 49.0 55.0 0.0 NaN 37 44 48 47 28 50 40 32 43 52 69 72 58 47 65 44 74 58 56 28 43 43 32 48 40 48 54 48 13 13 12 14 7 47 47 47 48 47 47 47 48 48 48 48 49 47 47 47 49 50 48 48 48 50 51 50 50 50 51
230611 D. Burns 18 1997-07-01 185 70 England Cambridge United 47 60 50000 CB Right 1 3 2 Medium/Medium Lean No RES 29.0 2015-07-01 2018.0 55.0 26.0 27.0 32.0 49.0 51.0 0.0 NaN 29 24 46 27 20 28 24 28 27 27 56 55 44 48 61 36 60 60 50 21 41 51 28 25 35 47 49 50 7 7 9 11 9 35 35 35 32 32 32 32 32 30 30 30 34 33 33 33 34 44 41 41 41 44 46 47 47 47 46
225118 S. Austin 18 1996-09-01 182 70 England Burton Albion 47 58 50000 ST Right 1 3 2 Medium/Medium Lean No RES 27.0 2014-08-15 2021.0 69.0 46.0 41.0 49.0 17.0 43.0 0.0 NaN 36 50 45 44 33 49 36 37 40 46 66 71 59 50 55 43 58 54 43 44 28 1 46 48 59 5 3 -3 13 10 11 13 10 47 47 47 49 48 48 48 49 48 48 48 48 42 42 42 48 35 31 31 31 35 33 27 27 27 33
228104 M. O'Connor 16 1998-07-31 181 77 Republic of Ireland Dundalk 47 58 50000 ST Right 1 3 3 Medium/Medium Normal No RES 26.0 2015-01-01 2016.0 55.0 48.0 35.0 46.0 22.0 43.0 0.0 NaN 30 55 49 39 40 41 37 27 30 48 52 52 59 48 63 42 74 53 40 45 31 15 48 45 43 13 11 9 10 8 8 15 16 47 47 47 45 46 46 46 45 45 45 45 44 40 40 40 44 34 32 32 32 34 33 30 30 30 33
229975 M. Argasiński 19 1995-08-01 187 74 Poland Cracovia 47 58 50000 CM Right 1 3 3 Medium/Medium Lean No SUB 17.0 2013-03-05 2016.0 54.0 34.0 50.0 46.0 46.0 53.0 0.0 NaN 39 30 50 59 30 44 34 36 52 44 53 54 53 48 56 47 60 44 56 31 53 43 32 51 39 48 42 55 10 8 11 10 13 43 43 43 44 44 44 44 44 46 46 46 46 47 47 47 46 47 49 49 49 47 48 49 49 49 48
208365 J. Dykes 20 1995-06-30 178 68 Republic of Ireland Sligo Rovers 47 57 50000 LB, CB Left 1 2 2 Low/Medium Normal No SUB 15.0 2012-01-01 2015.0 55.0 20.0 33.0 26.0 50.0 53.0 0.0 NaN 30 7 47 40 13 13 33 30 34 26 55 55 33 44 90 31 61 96 48 5 48 49 13 28 45 55 54 55 9 14 16 17 15 30 30 30 29 29 29 29 29 29 29 29 33 34 34 34 33 45 44 44 44 45 47 48 48 48 47
228745 C. Riascos 21 1994-06-19 178 74 Colombia Alianza Petrolera 47 57 50000 RB Right 1 4 2 Medium/Medium Normal No RES 4.0 2015-01-01 2021.0 59.0 26.0 28.0 31.0 51.0 60.0 0.0 NaN 17 25 43 34 30 24 30 28 26 26 55 63 84 46 64 40 68 59 74 13 50 56 15 31 33 54 55 62 17 13 11 13 11 34 34 34 31 31 31 31 31 31 31 31 32 33 33 33 32 45 44 44 44 45 47 51 51 51 47
222361 W. Randall 18 1997-05-02 180 65 England Swindon Town 47 56 40000 CM, LWB Right 1 3 2 Medium/Medium Lean No RES 21.0 2014-05-03 2017.0 64.0 39.0 49.0 49.0 38.0 40.0 0.0 NaN 41 37 43 54 36 44 45 44 51 46 65 63 73 48 60 41 55 48 37 40 36 28 44 51 46 34 45 47 15 9 8 8 15 45 45 45 48 47 47 47 48 49 49 49 49 47 47 47 49 45 44 44 44 45 45 41 41 41 45
225415 G. Casey 17 1997-07-08 182 70 England Stevenage 47 56 30000 RB Right 1 3 2 Medium/Medium Normal No RES 26.0 2014-01-01 2016.0 72.0 35.0 42.0 47.0 39.0 53.0 0.0 NaN 39 30 43 46 32 47 37 35 44 41 73 71 65 40 70 48 55 63 48 33 54 42 51 42 36 39 42 50 14 13 12 12 15 44 44 44 46 45 45 45 46 45 45 45 47 44 44 44 47 47 44 44 44 47 47 43 43 43 47
228794 M. Alnajrani 21 1993-07-23 175 70 Saudi Arabia Al Qadisiyah 47 56 50000 LW Right 1 3 3 Medium/Medium Lean No SUB 20.0 2012-01-01 2021.0 52.0 55.0 36.0 49.0 21.0 60.0 0.0 NaN 27 62 57 37 38 46 40 28 32 49 52 52 52 52 75 53 66 57 72 47 34 19 52 45 60 18 13 18 11 8 7 7 10 54 54 54 47 50 50 50 47 47 47 47 46 42 42 42 46 34 33 33 33 34 33 34 34 34 33
222914 A. Hawtin 20 1995-06-13 178 76 England Oxford United 47 55 50000 RM Right 1 3 2 Medium/Medium Lean No RES 32.0 2014-05-06 2021.0 64.0 45.0 41.0 52.0 23.0 40.0 0.0 NaN 42 26 41 43 46 51 46 33 39 55 67 62 58 37 67 64 42 42 43 41 32 24 45 42 49 11 24 22 10 12 9 11 10 47 47 47 48 48 48 48 48 46 46 46 47 41 41 41 47 37 34 34 34 37 36 31 31 31 36
225500 M. Loaiza 20 1995-04-06 182 79 Colombia Deportivo Cali 47 55 40000 GK Left 1 3 1 Medium/Medium Normal No SUB 32.0 2014-07-03 2021.0 0.0 0.0 0.0 0.0 0.0 0.0 45.0 NaN 11 13 1 28 -3 1 9 3 24 24 49 41 39 53 55 23 57 31 65 15 13 26 15 19 24 1 15 3 53 44 47 40 52 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
228719 A. Almutair 23 1992-06-12 165 66 Saudi Arabia Al Qadisiyah 47 55 50000 ST Left 1 3 2 Medium/Low Normal No SUB 7.0 2013-01-01 2021.0 60.0 40.0 50.0 46.0 43.0 48.0 0.0 NaN 37 33 48 60 39 43 40 37 52 42 68 54 55 48 79 55 62 55 39 40 59 45 54 49 37 38 44 41 8 7 11 11 10 47 47 47 47 48 48 48 47 49 49 49 49 49 49 49 49 47 48 48 48 47 47 46 46 46 47
228692 H. Jafari 23 1991-12-06 167 65 Saudi Arabia Al Qadisiyah 47 54 40000 CDM Left 1 3 2 Medium/Medium Lean No LCM 14.0 2014-01-01 2021.0 63.0 33.0 46.0 49.0 43.0 46.0 0.0 NaN 39 26 48 52 31 43 34 36 49 50 62 64 51 55 88 46 62 48 39 33 56 44 38 45 43 36 44 51 10 10 16 11 12 43 43 43 46 45 45 45 46 46 46 46 47 47 47 47 47 48 47 47 47 48 49 46 46 46 49
228722 A. Albalawi 24 1990-09-12 170 75 Saudi Arabia Al Qadisiyah 47 52 40000 CDM Right 1 3 3 Medium/Medium Normal No RES 12.0 2014-01-01 2021.0 51.0 37.0 57.0 50.0 34.0 62.0 0.0 NaN 47 36 41 64 37 44 44 43 67 55 52 50 54 44 68 41 58 47 74 29 50 21 45 54 45 34 36 42 7 8 12 11 12 46 46 46 48 47 47 47 48 51 51 51 50 51 51 51 50 44 47 47 47 44 43 43 43 43 43
210187 S. Amedo 32 1982-07-12 176 75 Saudi Arabia Al Wehda 47 47 20000 CB Right 1 3 2 Medium/Medium Normal No RES 17.0 2004-01-01 2020.0 32.0 20.0 25.0 31.0 46.0 65.0 0.0 NaN 20 14 45 25 26 26 26 21 28 26 33 32 49 43 62 38 65 58 72 13 56 43 16 28 32 43 49 49 9 15 12 12 12 29 29 29 26 26 26 26 26 26 26 26 28 30 30 30 28 38 40 40 40 38 40 47 47 47 40
225319 N. McLaughlin 17 1998-01-01 177 70 Scotland Partick Thistle FC 46 63 70000 ST, CAM Right 1 2 2 Medium/Medium Lean No RES 26.0 2015-05-26 2016.0 67.0 42.0 44.0 45.0 29.0 56.0 0.0 NaN 33 46 34 50 41 42 34 30 48 44 70 65 50 40 72 48 57 57 56 29 52 31 44 47 39 22 33 31 8 16 15 14 9 46 46 46 46 46 46 46 46 46 46 46 47 44 44 44 47 41 41 41 41 41 40 38 38 38 40
230721 S. Hanney 17 1998-02-19 182 74 Republic of Ireland Shamrock Rovers 46 63 60000 CM Right 1 3 2 Medium/Medium Lean No RES 19.0 2015-02-01 2018.0 69.0 42.0 43.0 48.0 44.0 51.0 0.0 NaN 37 57 42 41 42 48 42 50 37 49 76 76 49 35 58 45 64 83 31 52 35 49 35 45 50 47 55 32 14 5 12 13 14 46 46 46 48 47 47 47 48 47 47 47 49 46 46 46 49 48 46 46 46 48 48 45 45 45 48
225005 R. McManus 19 1996-06-15 178 70 Republic of Ireland Sligo Rovers 46 61 70000 CM Right 1 3 2 Medium/Medium Lean No SUB 26.0 2013-08-01 2015.0 69.0 43.0 44.0 48.0 41.0 48.0 0.0 NaN 40 41 45 49 40 46 33 33 45 47 72 66 56 43 62 49 54 60 44 44 41 39 46 44 38 41 44 36 15 11 6 8 14 47 47 47 48 48 48 48 48 48 48 48 49 46 46 46 49 47 44 44 44 47 46 43 43 43 46
225006 G. Armstrong 19 1996-01-28 180 71 Republic of Ireland Sligo Rovers 46 60 70000 CM Right 1 2 2 Medium/Medium Lean No SUB 20.0 2013-08-01 2015.0 65.0 39.0 45.0 46.0 42.0 47.0 0.0 NaN 41 34 45 48 39 46 33 34 45 43 66 65 53 46 56 45 54 57 42 40 43 38 46 47 44 41 46 36 7 6 9 7 7 45 45 45 47 46 46 46 47 46 46 46 48 46 46 46 48 46 44 44 44 46 46 43 43 43 46
225331 D. Henry 17 1997-09-12 180 65 England Peterborough United 46 58 40000 GK Right 1 2 1 Medium/Medium Normal No SUB 26.0 2014-08-01 2017.0 0.0 0.0 0.0 0.0 0.0 0.0 32.0 NaN 7 13 -3 29 3 7 7 15 24 11 29 34 36 52 71 15 50 29 42 15 26 1 -9 19 23 13 11 11 47 43 49 40 53 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
228155 C. Johns 18 1997-06-22 182 71 England Swindon Town 46 57 40000 GK Right 1 2 1 Medium/Medium Normal No RES 35.0 2015-03-14 2016.0 0.0 0.0 0.0 0.0 0.0 0.0 43.0 NaN 9 -3 1 22 5 -1 -1 15 11 25 43 43 36 52 64 15 58 -9 50 -1 7 25 11 13 22 3 13 9 43 47 39 42 52 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
229414 Ibargoien 19 1995-07-13 180 75 Spain SD Eibar 46 56 50000 CM Right 1 3 2 Medium/Medium Normal No RES 26.0 2015-07-01 2016.0 55.0 31.0 45.0 48.0 40.0 54.0 0.0 NaN 33 27 41 52 26 47 36 34 51 44 54 55 58 53 65 47 56 49 56 25 53 44 29 47 40 35 38 51 7 10 10 7 6 41 41 41 43 43 43 43 43 45 45 45 45 46 46 46 45 46 47 47 47 46 45 45 45 45 45
225158 T. Holland 18 1997-06-08 176 71 England Swindon Town 46 55 40000 RB Right 1 2 2 Medium/Medium Normal No RES 33.0 2014-08-23 2016.0 60.0 26.0 31.0 46.0 45.0 53.0 0.0 NaN 37 24 39 29 20 45 27 32 27 39 68 54 59 42 73 29 57 54 55 23 45 47 41 33 39 35 52 52 15 8 11 14 10 38 38 38 40 38 38 38 40 38 38 38 41 37 37 37 41 45 41 41 41 45 46 46 46 46 46
229393 S. Hornby 20 1995-02-14 188 80 England Burton Albion 46 55 50000 GK Right 1 3 1 Medium/Medium Normal No RES 28.0 2015-05-05 2016.0 0.0 0.0 0.0 0.0 0.0 0.0 46.0 NaN 12 13 13 27 13 14 13 14 19 15 46 46 38 51 51 22 58 24 66 17 18 22 12 21 26 17 11 15 49 43 45 41 48 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
224927 T. Smith 17 1998-02-25 176 70 England Swindon Town 46 54 40000 CM Right 1 2 3 Medium/Medium Normal No SUB 31.0 2014-08-09 2016.0 60.0 39.0 45.0 46.0 39.0 53.0 0.0 NaN 37 29 41 55 31 42 33 32 44 43 53 65 59 50 66 54 61 53 54 48 49 42 47 41 35 37 38 42 16 16 8 9 14 45 45 45 45 46 46 46 45 46 46 46 46 46 46 46 46 46 45 45 45 46 45 44 44 44 45
226099 A. Bishop 17 1998-06-12 182 71 England Mansfield Town 46 53 30000 GK Right 1 2 1 Medium/Medium Lean No RES 39.0 2014-07-01 2021.0 0.0 0.0 0.0 0.0 0.0 0.0 31.0 NaN 11 13 13 22 16 12 17 12 15 20 35 28 33 39 35 21 50 28 33 15 19 17 11 30 19 11 11 15 51 43 43 41 52 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
215734 E. Farrell 17 1997-07-06 182 72 Republic of Ireland Drogheda United 45 61 60000 GK Right 1 2 1 Medium/Medium Lean No SUB 40.0 2012-08-01 2020.0 0.0 0.0 0.0 0.0 0.0 0.0 44.0 NaN 7 13 7 26 -3 9 11 -1 25 25 46 43 33 46 51 9 48 45 52 -3 35 13 9 -3 23 5 -1 -1 46 45 46 45 46 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
224866 J. Akintunde 19 1996-03-29 175 75 England Cambridge United 45 57 60000 ST Right 1 3 2 Medium/Medium Stocky No RES 26.0 2014-01-01 2021.0 70.0 46.0 37.0 40.0 17.0 47.0 0.0 NaN 28 45 35 41 36 38 37 29 32 27 71 70 57 23 57 39 60 59 46 46 32 15 33 44 44 -1 -1 7 13 14 10 8 10 45 45 45 44 44 44 44 44 43 43 43 43 38 38 38 43 34 30 30 30 34 33 28 28 28 33
224868 M. Lowe 19 1996-03-11 179 74 England Cambridge United 45 55 50000 CM Right 1 3 2 Medium/Medium Lean No RES 27.0 2014-01-01 2021.0 69.0 43.0 49.0 48.0 36.0 44.0 0.0 NaN 43 45 36 56 43 48 41 37 55 38 68 69 74 38 64 46 58 48 46 34 28 27 47 43 44 36 37 47 11 6 9 6 13 46 46 46 48 47 47 47 48 48 48 48 49 45 45 45 49 44 41 41 41 44 44 39 39 39 44
228582 S. Sargeant 17 1997-09-23 183 67 England Leyton Orient 44 63 60000 GK Right 1 3 1 Medium/Medium Lean No RES 31.0 2014-09-01 2016.0 0.0 0.0 0.0 0.0 0.0 0.0 44.0 NaN 13 11 14 24 16 17 16 13 24 17 49 40 32 29 60 24 51 26 33 13 18 18 16 18 24 16 15 19 46 42 43 45 50 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
225873 R. Feely 18 1997-01-03 171 66 Republic of Ireland St. Patrick's Athletic 44 61 60000 CM Right 1 2 2 Medium/Medium Lean No SUB 30.0 2014-08-01 2015.0 70.0 38.0 41.0 48.0 40.0 42.0 0.0 NaN 44 40 40 35 34 51 37 35 45 50 75 74 54 35 52 39 49 73 30 37 38 40 42 47 42 40 43 37 11 10 14 9 17 42 42 42 46 45 45 45 46 45 45 45 48 44 44 44 48 46 42 42 42 46 46 39 39 39 46
227180 A. O'Kelly 17 1997-08-13 180 72 England Newport County 44 60 60000 RWB Right 1 2 2 Medium/Medium Lean No RES 50.0 2015-01-20 2016.0 65.0 22.0 33.0 37.0 43.0 50.0 0.0 NaN 48 13 41 42 28 11 30 27 13 37 63 66 71 17 78 15 52 55 74 15 9 44 40 28 35 49 52 51 12 16 9 8 9 34 34 34 37 34 34 34 37 34 34 34 37 32 32 32 37 44 38 38 38 44 45 43 43 43 45
227244 D. Clifton 18 1996-11-08 178 66 England Northampton Town 44 60 60000 CM Right 1 3 2 Medium/Medium Lean No RES 28.0 2015-01-24 2016.0 64.0 32.0 49.0 48.0 44.0 46.0 0.0 NaN 49 29 49 57 33 43 34 34 50 43 60 68 63 19 73 46 63 43 42 27 54 40 28 47 45 45 44 47 8 13 11 17 8 40 40 40 44 42 42 42 44 44 44 44 46 44 44 44 46 47 45 45 45 47 47 46 46 46 47
227881 S. McWilliams 16 1998-08-14 180 69 England Northampton Town 44 59 60000 CM Right 1 3 2 Medium/Medium Normal No RES 30.0 2015-02-21 2021.0 67.0 41.0 45.0 50.0 35.0 46.0 0.0 NaN 41 39 36 47 38 49 42 37 49 48 65 68 59 37 68 50 50 48 47 37 39 30 34 46 42 34 36 40 6 14 16 13 12 44 44 44 47 45 45 45 47 46 46 46 47 44 44 44 47 43 41 41 41 43 42 39 39 39 42
224867 R. Horne 19 1995-11-02 180 76 England Cambridge United 44 57 60000 CM Right 1 3 2 Medium/Medium Lean No RES 28.0 2014-01-01 2021.0 63.0 40.0 43.0 47.0 39.0 52.0 0.0 NaN 40 41 40 36 41 45 43 42 37 39 67 60 57 25 54 39 48 55 65 40 30 36 48 44 29 38 46 42 10 14 9 12 9 44 44 44 46 45 45 45 46 45 45 45 46 44 44 44 46 44 42 42 42 44 43 42 42 42 43
227910 L. Gooch 17 1997-11-25 181 65 England Luton Town 44 54 40000 GK Right 1 3 1 Medium/Medium Lean No RES 37.0 2015-02-21 2021.0 0.0 0.0 0.0 0.0 0.0 0.0 41.0 NaN 13 13 14 27 16 21 19 17 17 23 42 41 35 35 60 20 54 21 51 15 20 23 14 27 21 14 19 19 52 43 45 37 47 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
225339 S. Warburton 18 1996-10-10 171 64 England Northampton Town 44 51 30000 LB, LM Left 1 2 2 Medium/Medium Lean No RES 29.0 2014-08-24 2016.0 58.0 28.0 32.0 45.0 43.0 45.0 0.0 NaN 41 27 40 27 29 39 33 29 28 36 62 55 57 29 70 29 84 62 26 27 48 43 39 40 39 43 45 46 12 13 11 13 16 36 36 36 40 38 38 38 40 37 37 37 41 36 36 36 41 44 39 39 39 44 44 42 42 42 44
11728 B. Richardson 45 1969-08-05 185 77 England Wycombe Wanderers 44 44 10000 GK Right 1 2 1 Medium/Medium Stocky No SUB 13.0 2014-01-30 2021.0 0.0 0.0 0.0 0.0 0.0 0.0 25.0 NaN -3 -3 -1 -1 -1 -3 -1 -3 1 23 25 25 38 34 44 1 51 32 47 7 45 7 1 9 5 3 -1 1 37 55 37 59 33 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

10559 rows × 89 columns

In [50]:
draw_graphs(dataset=players_top_20_nations,title="Average player value per nationality",
            analyzed_feature="value_eur",groupby_feature="nationality",
            asc=True,xtitle="Value",ytitle="Nationality",
            stat_function="mean",
            orient='h',num_recs=20,marker_color="rebeccapurple")

Brazilian players have quite substantial prevailance in the price per player. Here interesting part is though the Netherlands have youngest players, their average value is quite high compared to other nationalities.

Now it is time to see where the top 300 most expensive players come from. This we will also visualize on a map. For this purpose we will import another dataset which contains the geographical coordinates of the capitals of countries. So, in this way we will position each player in his home country. Functions for spatial visualizations are taken from [3]

In [51]:
country_coords=pd.read_csv("data/gps_coords.csv")
In [52]:
country_coords.head()
Out[52]:
CountryName CapitalName CapitalLatitude CapitalLongitude CountryCode ContinentName
0 Somaliland Hargeisa 9.550000 44.050000 NaN Africa
1 South Georgia and South Sandwich Islands King Edward Point -54.283333 -36.500000 GS Antarctica
2 French Southern and Antarctic Lands Port-aux-Français -49.350000 70.216667 TF Antarctica
3 Palestine Jerusalem 31.766667 35.233333 PS Asia
4 Aland Islands Mariehamn 60.116667 19.900000 AX Europe
In [53]:
country_coords.describe()
Out[53]:
CapitalLatitude CapitalLongitude
count 245.000000 245.000000
mean 17.335901 12.905790
std 25.739778 72.941443
min -54.283333 -175.200000
25% 0.316667 -36.500000
50% 16.700000 15.300000
75% 38.883333 45.333333
max 78.216667 179.216667
In [54]:
country_coords.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 245 entries, 0 to 244
Data columns (total 6 columns):
CountryName         245 non-null object
CapitalName         241 non-null object
CapitalLatitude     245 non-null float64
CapitalLongitude    245 non-null float64
CountryCode         242 non-null object
ContinentName       245 non-null object
dtypes: float64(2), object(4)
memory usage: 11.6+ KB

From this analysis we can see that there is no missing data. The dataset has column for country, so we can directly join both datasets and take the coordinates.

In [55]:
top_300_val_players=players_16.groupby(["short_name","nationality"])["value_eur"].sum().sort_values(ascending=False)[:300]
In [56]:
top_300_val_players=pd.DataFrame(top_300_val_players,index=None).reset_index()
In [57]:
top_300_val_players=pd.DataFrame(top_300_val_players)
top_300_val_players.merge(country_coords, left_on='nationality', right_on='CountryName')
Out[57]:
short_name nationality value_eur CountryName CapitalName CapitalLatitude CapitalLongitude CountryCode ContinentName
0 L. Messi Argentina 111000000 Argentina Buenos Aires -34.583333 -58.666667 AR South America
1 S. Agüero Argentina 47500000 Argentina Buenos Aires -34.583333 -58.666667 AR South America
2 J. Pastore Argentina 34500000 Argentina Buenos Aires -34.583333 -58.666667 AR South America
3 G. Higuaín Argentina 34500000 Argentina Buenos Aires -34.583333 -58.666667 AR South America
4 C. Tévez Argentina 34500000 Argentina Buenos Aires -34.583333 -58.666667 AR South America
5 A. Di María Argentina 34000000 Argentina Buenos Aires -34.583333 -58.666667 AR South America
6 N. Gaitán Argentina 25500000 Argentina Buenos Aires -34.583333 -58.666667 AR South America
7 N. Otamendi Argentina 24000000 Argentina Buenos Aires -34.583333 -58.666667 AR South America
8 R. Pereyra Argentina 23000000 Argentina Buenos Aires -34.583333 -58.666667 AR South America
9 E. Garay Argentina 21000000 Argentina Buenos Aires -34.583333 -58.666667 AR South America
10 M. Musacchio Argentina 20000000 Argentina Buenos Aires -34.583333 -58.666667 AR South America
11 E. Banega Argentina 20000000 Argentina Buenos Aires -34.583333 -58.666667 AR South America
12 E. Salvio Argentina 18000000 Argentina Buenos Aires -34.583333 -58.666667 AR South America
13 P. Piatti Argentina 17500000 Argentina Buenos Aires -34.583333 -58.666667 AR South America
14 M. Rojo Argentina 17500000 Argentina Buenos Aires -34.583333 -58.666667 AR South America
15 L. Biglia Argentina 15000000 Argentina Buenos Aires -34.583333 -58.666667 AR South America
16 L. Vietto Argentina 15000000 Argentina Buenos Aires -34.583333 -58.666667 AR South America
17 J. Mascherano Argentina 15000000 Argentina Buenos Aires -34.583333 -58.666667 AR South America
18 M. Icardi Argentina 14500000 Argentina Buenos Aires -34.583333 -58.666667 AR South America
19 P. Zabaleta Argentina 14000000 Argentina Buenos Aires -34.583333 -58.666667 AR South America
20 P. Dybala Argentina 14000000 Argentina Buenos Aires -34.583333 -58.666667 AR South America
21 Cristiano Ronaldo Portugal 85500000 Portugal Lisbon 38.716667 -9.133333 PT Europe
22 João Moutinho Portugal 23000000 Portugal Lisbon 38.716667 -9.133333 PT Europe
23 Nani Portugal 21000000 Portugal Lisbon 38.716667 -9.133333 PT Europe
24 William Carvalho Portugal 20500000 Portugal Lisbon 38.716667 -9.133333 PT Europe
25 André Gomes Portugal 20000000 Portugal Lisbon 38.716667 -9.133333 PT Europe
26 A. Lopes Portugal 19500000 Portugal Lisbon 38.716667 -9.133333 PT Europe
27 Danny Portugal 17000000 Portugal Lisbon 38.716667 -9.133333 PT Europe
28 Adrien Silva Portugal 17000000 Portugal Lisbon 38.716667 -9.133333 PT Europe
29 Pepe Portugal 17000000 Portugal Lisbon 38.716667 -9.133333 PT Europe
30 Rui Patrício Portugal 16500000 Portugal Lisbon 38.716667 -9.133333 PT Europe
31 Bernardo Silva Portugal 14000000 Portugal Lisbon 38.716667 -9.133333 PT Europe
32 Quaresma Portugal 14000000 Portugal Lisbon 38.716667 -9.133333 PT Europe
33 E. Hazard Belgium 74000000 Belgium Brussels 50.833333 4.333333 BE Europe
34 K. De Bruyne Belgium 50500000 Belgium Brussels 50.833333 4.333333 BE Europe
35 T. Courtois Belgium 44000000 Belgium Brussels 50.833333 4.333333 BE Europe
36 C. Benteke Belgium 27000000 Belgium Brussels 50.833333 4.333333 BE Europe
37 R. Nainggolan Belgium 26500000 Belgium Brussels 50.833333 4.333333 BE Europe
38 V. Kompany Belgium 25000000 Belgium Brussels 50.833333 4.333333 BE Europe
39 D. Mertens Belgium 22000000 Belgium Brussels 50.833333 4.333333 BE Europe
40 A. Witsel Belgium 20000000 Belgium Brussels 50.833333 4.333333 BE Europe
41 R. Lukaku Belgium 20000000 Belgium Brussels 50.833333 4.333333 BE Europe
42 T. Alderweireld Belgium 17000000 Belgium Brussels 50.833333 4.333333 BE Europe
43 J. Vertonghen Belgium 16000000 Belgium Brussels 50.833333 4.333333 BE Europe
44 Neymar Brazil 71500000 Brazil Brasilia -15.783333 -47.916667 BR South America
45 Oscar Brazil 38000000 Brazil Brasilia -15.783333 -47.916667 BR South America
46 Thiago Silva Brazil 38000000 Brazil Brasilia -15.783333 -47.916667 BR South America
47 Coutinho Brazil 38000000 Brazil Brasilia -15.783333 -47.916667 BR South America
48 Danilo Brazil 33400000 Brazil Brasilia -15.783333 -47.916667 BR South America
49 Marcelo Brazil 32700000 Brazil Brasilia -15.783333 -47.916667 BR South America
50 Hulk Brazil 28500000 Brazil Brasilia -15.783333 -47.916667 BR South America
51 Maicon Brazil 27475000 Brazil Brasilia -15.783333 -47.916667 BR South America
52 Roberto Firmino Brazil 26500000 Brazil Brasilia -15.783333 -47.916667 BR South America
53 Lucas Brazil 26000000 Brazil Brasilia -15.783333 -47.916667 BR South America
54 Alex Teixeira Brazil 25000000 Brazil Brasilia -15.783333 -47.916667 BR South America
55 Rafinha Brazil 23675000 Brazil Brasilia -15.783333 -47.916667 BR South America
56 Willian Brazil 23500000 Brazil Brasilia -15.783333 -47.916667 BR South America
57 Alex Sandro Brazil 22000000 Brazil Brasilia -15.783333 -47.916667 BR South America
58 Miranda Brazil 21725000 Brazil Brasilia -15.783333 -47.916667 BR South America
59 Naldo Brazil 21600000 Brazil Brasilia -15.783333 -47.916667 BR South America
60 Douglas Costa Brazil 21500000 Brazil Brasilia -15.783333 -47.916667 BR South America
61 David Luiz Brazil 21000000 Brazil Brasilia -15.783333 -47.916667 BR South America
62 Marquinhos Brazil 20000000 Brazil Brasilia -15.783333 -47.916667 BR South America
63 Luiz Gustavo Brazil 19000000 Brazil Brasilia -15.783333 -47.916667 BR South America
64 Diego Alves Brazil 19000000 Brazil Brasilia -15.783333 -47.916667 BR South America
65 Allan Brazil 18000000 Brazil Brasilia -15.783333 -47.916667 BR South America
66 Maurício Brazil 17600000 Brazil Brasilia -15.783333 -47.916667 BR South America
67 Jonas Brazil 17500000 Brazil Brasilia -15.783333 -47.916667 BR South America
68 Dani Alves Brazil 16000000 Brazil Brasilia -15.783333 -47.916667 BR South America
69 Leandro Castán Brazil 16000000 Brazil Brasilia -15.783333 -47.916667 BR South America
70 Raffael Brazil 15500000 Brazil Brasilia -15.783333 -47.916667 BR South America
71 Felipe Anderson Brazil 15500000 Brazil Brasilia -15.783333 -47.916667 BR South America
72 Gabriel Brazil 14800000 Brazil Brasilia -15.783333 -47.916667 BR South America
73 Taison Brazil 14500000 Brazil Brasilia -15.783333 -47.916667 BR South America
74 Filipe Luís Brazil 14500000 Brazil Brasilia -15.783333 -47.916667 BR South America
75 Rafael Brazil 14400000 Brazil Brasilia -15.783333 -47.916667 BR South America
76 Aderllan Santos Brazil 14000000 Brazil Brasilia -15.783333 -47.916667 BR South America
77 L. Suárez Uruguay 69000000 Uruguay Montevideo -34.850000 -56.166667 UY South America
78 E. Cavani Uruguay 33500000 Uruguay Montevideo -34.850000 -56.166667 UY South America
79 D. Godín Uruguay 25000000 Uruguay Montevideo -34.850000 -56.166667 UY South America
80 F. Muslera Uruguay 17000000 Uruguay Montevideo -34.850000 -56.166667 UY South America
81 J. Rodríguez Colombia 63020000 Colombia Bogota 4.600000 -74.083333 CO South America
82 J. Martínez Colombia 29800000 Colombia Bogota 4.600000 -74.083333 CO South America
83 Falcao Colombia 23000000 Colombia Bogota 4.600000 -74.083333 CO South America
84 J. Cuadrado Colombia 21700000 Colombia Bogota 4.600000 -74.083333 CO South America
85 C. Bacca Colombia 20500000 Colombia Bogota 4.600000 -74.083333 CO South America
86 M. Neuer Germany 58000000 Germany Berlin 52.516667 13.400000 DE Europe
87 T. Kroos Germany 54500000 Germany Berlin 52.516667 13.400000 DE Europe
88 M. Özil Germany 52500000 Germany Berlin 52.516667 13.400000 DE Europe
89 T. Müller Germany 47500000 Germany Berlin 52.516667 13.400000 DE Europe
90 M. Reus Germany 45500000 Germany Berlin 52.516667 13.400000 DE Europe
91 J. Boateng Germany 45000000 Germany Berlin 52.516667 13.400000 DE Europe
92 M. Hummels Germany 39000000 Germany Berlin 52.516667 13.400000 DE Europe
93 M. Götze Germany 38000000 Germany Berlin 52.516667 13.400000 DE Europe
94 B. Schweinsteiger Germany 35000000 Germany Berlin 52.516667 13.400000 DE Europe
95 B. Leno Germany 32500000 Germany Berlin 52.516667 13.400000 DE Europe
96 P. Lahm Germany 29500000 Germany Berlin 52.516667 13.400000 DE Europe
97 K. Bellarabi Germany 27500000 Germany Berlin 52.516667 13.400000 DE Europe
98 I. Gündoğan Germany 26000000 Germany Berlin 52.516667 13.400000 DE Europe
99 M. ter Stegen Germany 23500000 Germany Berlin 52.516667 13.400000 DE Europe
... ... ... ... ... ... ... ... ... ...
175 Bojan Spain 21500000 Spain Madrid 40.400000 -3.683333 ES Europe
176 Paco Alcácer Spain 21500000 Spain Madrid 40.400000 -3.683333 ES Europe
177 Vitolo Spain 21375000 Spain Madrid 40.400000 -3.683333 ES Europe
178 Ander Herrera Spain 20500000 Spain Madrid 40.400000 -3.683333 ES Europe
179 Nolito Spain 20000000 Spain Madrid 40.400000 -3.683333 ES Europe
180 Azpilicueta Spain 19500000 Spain Madrid 40.400000 -3.683333 ES Europe
181 Muniaín Spain 19500000 Spain Madrid 40.400000 -3.683333 ES Europe
182 Casillas Spain 19000000 Spain Madrid 40.400000 -3.683333 ES Europe
183 Carvajal Spain 18500000 Spain Madrid 40.400000 -3.683333 ES Europe
184 Camacho Spain 18025000 Spain Madrid 40.400000 -3.683333 ES Europe
185 Borja Valero Spain 18000000 Spain Madrid 40.400000 -3.683333 ES Europe
186 Gayà Spain 17000000 Spain Madrid 40.400000 -3.683333 ES Europe
187 José Callejón Spain 16500000 Spain Madrid 40.400000 -3.683333 ES Europe
188 Negredo Spain 16500000 Spain Madrid 40.400000 -3.683333 ES Europe
189 Iturraspe Spain 16500000 Spain Madrid 40.400000 -3.683333 ES Europe
190 Marc Bartra Spain 16500000 Spain Madrid 40.400000 -3.683333 ES Europe
191 Iborra Spain 16000000 Spain Madrid 40.400000 -3.683333 ES Europe
192 Iñigo Martínez Spain 16000000 Spain Madrid 40.400000 -3.683333 ES Europe
193 Óliver Torres Spain 15500000 Spain Madrid 40.400000 -3.683333 ES Europe
194 Víctor Ruíz Spain 15000000 Spain Madrid 40.400000 -3.683333 ES Europe
195 Juanfran Spain 14800000 Spain Madrid 40.400000 -3.683333 ES Europe
196 Sergio Asenjo Spain 14500000 Spain Madrid 40.400000 -3.683333 ES Europe
197 De Marcos Spain 14500000 Spain Madrid 40.400000 -3.683333 ES Europe
198 Mikel San José Spain 14500000 Spain Madrid 40.400000 -3.683333 ES Europe
199 Victor Valdés Spain 14500000 Spain Madrid 40.400000 -3.683333 ES Europe
200 Diego López Spain 14500000 Spain Madrid 40.400000 -3.683333 ES Europe
201 Kiko Casilla Spain 14500000 Spain Madrid 40.400000 -3.683333 ES Europe
202 Rodrigo Spain 14000000 Spain Madrid 40.400000 -3.683333 ES Europe
203 A. Sánchez Chile 47000000 Chile Santiago -33.450000 -70.666667 CL South America
204 A. Vidal Chile 37500000 Chile Santiago -33.450000 -70.666667 CL South America
205 C. Bravo Chile 18475000 Chile Santiago -33.450000 -70.666667 CL South America
206 G. Medel Chile 15500000 Chile Santiago -33.450000 -70.666667 CL South America
207 L. Modrić Croatia 41500000 Croatia Zagreb 45.800000 16.000000 HR Europe
208 I. Rakitić Croatia 31500000 Croatia Zagreb 45.800000 16.000000 HR Europe
209 M. Mandžukić Croatia 23000000 Croatia Zagreb 45.800000 16.000000 HR Europe
210 D. Subašić Croatia 14000000 Croatia Zagreb 45.800000 16.000000 HR Europe
211 Z. Ibrahimović Sweden 40500000 Sweden Stockholm 59.333333 18.050000 SE Europe
212 M. Verratti Italy 38000000 Italy Rome 41.900000 12.483333 IT Europe
213 G. Chiellini Italy 32500000 Italy Rome 41.900000 12.483333 IT Europe
214 C. Marchisio Italy 26500000 Italy Rome 41.900000 12.483333 IT Europe
215 L. Insigne Italy 23000000 Italy Rome 41.900000 12.483333 IT Europe
216 A. Candreva Italy 23000000 Italy Rome 41.900000 12.483333 IT Europe
217 M. Perin Italy 20500000 Italy Rome 41.900000 12.483333 IT Europe
218 S. El Shaarawy Italy 20000000 Italy Rome 41.900000 12.483333 IT Europe
219 D. Berardi Italy 19500000 Italy Rome 41.900000 12.483333 IT Europe
220 M. Balotelli Italy 18500000 Italy Rome 41.900000 12.483333 IT Europe
221 L. Bonucci Italy 18500000 Italy Rome 41.900000 12.483333 IT Europe
222 M. Darmian Italy 17500000 Italy Rome 41.900000 12.483333 IT Europe
223 S. Sirigu Italy 17500000 Italy Rome 41.900000 12.483333 IT Europe
224 S. Giovinco Italy 16500000 Italy Rome 41.900000 12.483333 IT Europe
225 M. Parolo Italy 15000000 Italy Rome 41.900000 12.483333 IT Europe
226 D. De Rossi Italy 15000000 Italy Rome 41.900000 12.483333 IT Europe
227 G. Rossi Italy 14500000 Italy Rome 41.900000 12.483333 IT Europe
228 D. Alaba Austria 35500000 Austria Vienna 48.200000 16.366667 AT Europe
229 C. Eriksen Denmark 31000000 Denmark Copenhagen 55.666667 12.583333 DK Europe
230 S. Kjær Denmark 18000000 Denmark Copenhagen 55.666667 12.583333 DK Europe
231 N. Matić Serbia 29500000 Serbia Belgrade 44.833333 20.500000 RS Europe
232 F. Djordjevic Serbia 17000000 Serbia Belgrade 44.833333 20.500000 RS Europe
233 N. Maksimović Serbia 17000000 Serbia Belgrade 44.833333 20.500000 RS Europe
234 N. Subotić Serbia 14000000 Serbia Belgrade 44.833333 20.500000 RS Europe
235 A. Ljajić Serbia 14000000 Serbia Belgrade 44.833333 20.500000 RS Europe
236 A. Turan Turkey 26950000 Turkey Ankara 39.933333 32.866667 TR Europe
237 N. Şahin Turkey 19500000 Turkey Ankara 39.933333 32.866667 TR Europe
238 G. Töre Turkey 19000000 Turkey Ankara 39.933333 32.866667 TR Europe
239 O. Toprak Turkey 17000000 Turkey Ankara 39.933333 32.866667 TR Europe
240 R. Rodriguez Switzerland 26600000 Switzerland Bern 46.916667 7.466667 CH Europe
241 G. Xhaka Switzerland 21500000 Switzerland Bern 46.916667 7.466667 CH Europe
242 Y. Sommer Switzerland 20000000 Switzerland Bern 46.916667 7.466667 CH Europe
243 X. Shaqiri Switzerland 18500000 Switzerland Bern 46.916667 7.466667 CH Europe
244 R. Bürki Switzerland 16000000 Switzerland Bern 46.916667 7.466667 CH Europe
245 M. Hamšik Slovakia 26500000 Slovakia Bratislava 48.150000 17.116667 SK Europe
246 Y. Konoplyanka Ukraine 25500000 Ukraine Kyiv 50.433333 30.516667 UA Europe
247 C. Vela Mexico 24500000 Mexico Mexico City 19.433333 -99.133333 MX Central America
248 J. Hernández Mexico 16850000 Mexico Mexico City 19.433333 -99.133333 MX Central America
249 P. Aubameyang Gabon 24500000 Gabon Libreville 0.383333 9.450000 GA Africa
250 P. Čech Czech Republic 24000000 Czech Republic Prague 50.083333 14.466667 CZ Europe
251 S. Kagawa Japan 23500000 Japan Tokyo 35.683333 139.750000 JP Asia
252 M. Benatia Morocco 23000000 Morocco Rabat 34.016667 -6.816667 MA Africa
253 K. Manolas Greece 22500000 Greece Athens 37.983333 23.733333 GR Europe
254 Sokratis Greece 21500000 Greece Athens 37.983333 23.733333 GR Europe
255 A. Samaris Greece 15000000 Greece Athens 37.983333 23.733333 GR Europe
256 R. Eremenko Finland 22000000 Finland Helsinki 60.166667 24.933333 FI Europe
257 S. Feghouli Algeria 21500000 Algeria Algiers 36.750000 3.050000 DZ Africa
258 Y. Brahimi Algeria 18000000 Algeria Algiers 36.750000 3.050000 DZ Africa
259 M. Salah Egypt 20500000 Egypt Cairo 30.050000 31.250000 EG Africa
260 S. Jovetić Montenegro 20500000 Montenegro Podgorica 42.433333 19.266667 ME Europe
261 S. Savić Montenegro 18500000 Montenegro Podgorica 42.433333 19.266667 ME Europe
262 H. Mkhitaryan Armenia 19500000 Armenia Yerevan 40.166667 44.500000 AM Europe
263 J. Oblak Slovenia 19500000 Slovenia Ljubljana 46.050000 14.516667 SI Europe
264 S. Handanovič Slovenia 19000000 Slovenia Ljubljana 46.050000 14.516667 SI Europe
265 A. Carrillo Peru 19000000 Peru Lima -12.050000 -77.050000 PE South America
266 N. Nkoulou Cameroon 18500000 Cameroon Yaounde 3.866667 11.516667 CM Africa
267 J. Matip Cameroon 15500000 Cameroon Yaounde 3.866667 11.516667 CM Africa
268 V. Aboubakar Cameroon 14000000 Cameroon Yaounde 3.866667 11.516667 CM Africa
269 O. Shatov Russia 18500000 Russia Moscow 55.750000 37.600000 RU Europe
270 I. Smolnikov Russia 14500000 Russia Moscow 55.750000 37.600000 RU Europe
271 I. Akinfeev Russia 14500000 Russia Moscow 55.750000 37.600000 RU Europe
272 S. Rondón Venezuela 18000000 Venezuela Caracas 10.483333 -66.866667 VE South America
273 M. Bradley United States 15500000 United States Washington 38.883333 -77.000000 US Central America
274 B. Natcho Israel 15500000 Israel Jerusalem 31.766667 35.233333 IL Asia

275 rows × 9 columns

Though we chose 300 players, in the resulting merged dataset we can see only 275 records. This might mean that some of the countries' name do not coinside in both tables. Let's explore.

In [58]:
top_300_val_players.nationality.unique()
Out[58]:
array(['Argentina', 'Portugal', 'Belgium', 'Brazil', 'Uruguay',
       'Colombia', 'Germany', 'Netherlands', 'Wales', 'Poland', 'France',
       'Spain', 'Chile', 'Croatia', 'Sweden', 'England', 'Italy',
       'Austria', 'Bosnia Herzegovina', 'Denmark', 'Serbia', 'Turkey',
       'Switzerland', 'Slovakia', 'Ukraine', 'Ivory Coast', 'Mexico',
       'Gabon', 'Czech Republic', 'Japan', 'Morocco', 'Greece', 'Finland',
       'Algeria', 'Egypt', 'Montenegro', 'Armenia', 'Slovenia', 'Peru',
       'Cameroon', 'Russia', 'Venezuela', 'Republic of Ireland',
       'United States', 'Israel'], dtype=object)
In [59]:
country_coords.CountryName.unique()
Out[59]:
array(['Somaliland', 'South Georgia and South Sandwich Islands',
       'French Southern and Antarctic Lands', 'Palestine',
       'Aland Islands', 'Nauru', 'Saint Martin', 'Tokelau',
       'Western Sahara', 'Afghanistan', 'Albania', 'Algeria',
       'American Samoa', 'Andorra', 'Angola', 'Anguilla',
       'Antigua and Barbuda', 'Argentina', 'Armenia', 'Aruba',
       'Australia', 'Austria', 'Azerbaijan', 'Bahamas', 'Bahrain',
       'Bangladesh', 'Barbados', 'Belarus', 'Belgium', 'Belize', 'Benin',
       'Bermuda', 'Bhutan', 'Bolivia', 'Bosnia and Herzegovina',
       'Botswana', 'Brazil', 'British Virgin Islands',
       'Brunei Darussalam', 'Bulgaria', 'Burkina Faso', 'Myanmar',
       'Burundi', 'Cambodia', 'Cameroon', 'Canada', 'Cape Verde',
       'Cayman Islands', 'Central African Republic', 'Chad', 'Chile',
       'China', 'Christmas Island', 'Cocos Islands', 'Colombia',
       'Comoros', 'Democratic Republic of the Congo', 'Republic of Congo',
       'Cook Islands', 'Costa Rica', "Cote d'Ivoire", 'Croatia', 'Cuba',
       'Curaçao', 'Cyprus', 'Czech Republic', 'Denmark', 'Djibouti',
       'Dominica', 'Dominican Republic', 'Ecuador', 'Egypt',
       'El Salvador', 'Equatorial Guinea', 'Eritrea', 'Estonia',
       'Ethiopia', 'Falkland Islands', 'Faroe Islands', 'Fiji', 'Finland',
       'France', 'French Polynesia', 'Gabon', 'The Gambia', 'Georgia',
       'Germany', 'Ghana', 'Gibraltar', 'Greece', 'Greenland', 'Grenada',
       'Guam', 'Guatemala', 'Guernsey', 'Guinea', 'Guinea-Bissau',
       'Guyana', 'Haiti', 'Vatican City', 'Honduras', 'Hungary',
       'Iceland', 'India', 'Indonesia', 'Iran', 'Iraq', 'Ireland',
       'Isle of Man', 'Israel', 'Italy', 'Jamaica', 'Japan', 'Jersey',
       'Jordan', 'Kazakhstan', 'Kenya', 'Kiribati', 'North Korea',
       'South Korea', 'Kosovo', 'Kuwait', 'Kyrgyzstan', 'Laos', 'Latvia',
       'Lebanon', 'Lesotho', 'Liberia', 'Libya', 'Liechtenstein',
       'Lithuania', 'Luxembourg', 'Macedonia', 'Madagascar', 'Malawi',
       'Malaysia', 'Maldives', 'Mali', 'Malta', 'Marshall Islands',
       'Mauritania', 'Mauritius', 'Mexico',
       'Federated States of Micronesia', 'Moldova', 'Monaco', 'Mongolia',
       'Montenegro', 'Montserrat', 'Morocco', 'Mozambique', 'Namibia',
       'Nepal', 'Netherlands', 'New Caledonia', 'New Zealand',
       'Nicaragua', 'Niger', 'Nigeria', 'Niue', 'Norfolk Island',
       'Northern Mariana Islands', 'Norway', 'Oman', 'Pakistan', 'Palau',
       'Panama', 'Papua New Guinea', 'Paraguay', 'Peru', 'Philippines',
       'Pitcairn Islands', 'Poland', 'Portugal', 'Puerto Rico', 'Qatar',
       'Romania', 'Russia', 'Rwanda', 'Saint Barthelemy', 'Saint Helena',
       'Saint Kitts and Nevis', 'Saint Lucia',
       'Saint Pierre and Miquelon', 'Saint Vincent and the Grenadines',
       'Samoa', 'San Marino', 'Sao Tome and Principe', 'Saudi Arabia',
       'Senegal', 'Serbia', 'Seychelles', 'Sierra Leone', 'Singapore',
       'Sint Maarten', 'Slovakia', 'Slovenia', 'Solomon Islands',
       'Somalia', 'South Africa', 'South Sudan', 'Spain', 'Sri Lanka',
       'Sudan', 'Suriname', 'Svalbard', 'Swaziland', 'Sweden',
       'Switzerland', 'Syria', 'Taiwan', 'Tajikistan', 'Tanzania',
       'Thailand', 'Timor-Leste', 'Togo', 'Tonga', 'Trinidad and Tobago',
       'Tunisia', 'Turkey', 'Turkmenistan', 'Turks and Caicos Islands',
       'Tuvalu', 'Uganda', 'Ukraine', 'United Arab Emirates',
       'United Kingdom', 'United States', 'Uruguay', 'Uzbekistan',
       'Vanuatu', 'Venezuela', 'Vietnam', 'US Virgin Islands',
       'Wallis and Futuna', 'Yemen', 'Zambia', 'Zimbabwe',
       'US Minor Outlying Islands', 'Antarctica', 'Northern Cyprus',
       'Hong Kong', 'Heard Island and McDonald Islands',
       'British Indian Ocean Territory', 'Macau'], dtype=object)
In [60]:
top_300_set=set(top_300_val_players.nationality.unique())
country_set=set(country_coords.CountryName.unique())
In [61]:
top_300_set.issubset(country_set)
Out[61]:
False

False shows that our suspicion for not coinciding names is true and by subtracting two sets we can identify the differences.

In [62]:
top_300_set-country_set
Out[62]:
{'Bosnia Herzegovina',
 'England',
 'Ivory Coast',
 'Republic of Ireland',
 'Wales'}

Let's see how these countries' names are spelt in the country_set. And here is the difference: "Bosnia Herzegovina" is instead of 'Bosnia and Herzegovina' and "Cote d'Ivoire" stands for "Ivory coast". England and Wales do not exist in the database as separate countries, which is logical. Let's change the names of "Bosnia and Herzegovina", "Cote d'Ivoire" and "Ireland" (in the dataset the coordinates are thos of Dublin, so we should change it to Republic of Ireland). For England, Northern Ireland and Wales we will add records by using a dictionary.

In [63]:
country_coords.CountryName=country_coords.CountryName.str.replace("Bosnia and Herzegovina","Bosnia Herzegovina")
country_coords.CountryName=country_coords.CountryName.str.replace("Cote d'Ivoire","Ivory Coast")
country_coords.CountryName=country_coords.CountryName.str.replace("Ireland","Republic of Ireland")
In [64]:
coords_dict={"eng":{"CountryName":"England",
                    "CapitalName":"London",
                    "CapitalLatitude":"51.500000",
                    "CapitalLongitude":"-0.083333",
                    "CountryCode":"NaN",
                    "ContinentName":"Europe"},
             "wal":{"CountryName":"Wales",
                    "CapitalName":"Cardiff",
                    "CapitalLatitude":"51.481583",
                    "CapitalLongitude":"-3.179090",
                    "CountryCode":"NaN",
                    "ContinentName":"Europe"},
            "irl":{"CountryName":"Northern Ireland",
                    "CapitalName":"Belfast",
                    "CapitalLatitude":" 54.607868",
                    "CapitalLongitude":"-5.926437",
                    "CountryCode":"NaN",
                    "ContinentName":"Europe"}}
             
In [65]:
for key,value in coords_dict.items():
    country_coords=country_coords.append(coords_dict[key],ignore_index=True)

Now, let's do the merge again.

In [66]:
players_coordinates=top_300_val_players.merge(country_coords, left_on='nationality', right_on='CountryName')
In [67]:
players_coordinates.shape
Out[67]:
(300, 9)

This time the records are exactly 300.

In [68]:
players_coordinates.CapitalLongitude=players_coordinates.CapitalLongitude.astype(float)
players_coordinates.CapitalLatitude=players_coordinates.CapitalLatitude.astype(float)
In [69]:
plt.figure(figsize = (12, 10))
m = Basemap(projection = "merc", llcrnrlat = -73, llcrnrlon = -180, urcrnrlat = 80, urcrnrlon = 180)
x, y = None, None
x, y = m(players_coordinates.CapitalLongitude.tolist(),players_coordinates.CapitalLatitude.tolist())
m.plot(x,y,"o",color="red",markersize=2)
m.drawcoastlines()
m.drawcountries()
m.fillcontinents(color = "lightgreen", lake_color = "aqua")
m.drawmapboundary(fill_color = "aqua")
plt.show()
C:\Users\mbararova\AppData\Local\Continuum\anaconda3\lib\site-packages\ipykernel_launcher.py:2: MatplotlibDeprecationWarning:


The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.

C:\Users\mbararova\AppData\Local\Continuum\anaconda3\lib\site-packages\ipykernel_launcher.py:7: MatplotlibDeprecationWarning:


The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.

In [70]:
def plot_players_densities(coord_data, title = "Players densities"):
    plt.figure(figsize = (15, 10))
    m = Basemap(projection = "merc", llcrnrlat = -73, llcrnrlon = -180, urcrnrlat = 80, urcrnrlon = 180)

    # Prepare histogram bins
    num_bins = 100
    lon_bins = np.linspace(-180, 180, num_bins + 1)
    lat_bins = np.linspace(-90, 90, num_bins + 1)

    # Create 2D histogram values
    density, x_breaks, y_breaks = np.histogram2d(
        coord_data.CapitalLatitude,
        coord_data.CapitalLongitude, 
        [lat_bins, lon_bins])

    # Create the basis of the histogram - the (x, y) value pairs
    # and map them to 2D distances
    lon_bins_2d, lat_bins_2d = np.meshgrid(lon_bins, lat_bins)
    x, y = m(lon_bins_2d, lat_bins_2d)

    m.drawcoastlines()
    m.drawcountries()
    m.pcolormesh(x, y, density)
    m.colorbar()
    plt.title(title)
    plt.show()
In [71]:
plot_players_densities(players_coordinates)
C:\Users\mbararova\AppData\Local\Continuum\anaconda3\lib\site-packages\ipykernel_launcher.py:3: MatplotlibDeprecationWarning:


The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.

C:\Users\mbararova\AppData\Local\Continuum\anaconda3\lib\site-packages\ipykernel_launcher.py:22: MatplotlibDeprecationWarning:


The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.

Most of the 300 highest paid professionals are from Spain. We will investigate little bit by checking what si their number in the second group of 300 players.

In [72]:
second_300_val_players=players_16.groupby(["short_name","nationality"])["value_eur"].sum().sort_values(ascending=False)[301:600]
In [73]:
second_300_val_players=pd.DataFrame(second_300_val_players,index=None).reset_index()
In [74]:
second_300_val_players.groupby("nationality")["nationality"].count().sort_values(ascending=False).head(10)
Out[74]:
nationality
Spain          43
France         25
Brazil         23
Italy          20
England        19
Germany        16
Argentina      14
Portugal       14
Netherlands    11
Turkey         10
Name: nationality, dtype: int64

Again we can see that spanish players are the biggest number in this group. Though the country with highest number of representatives in the database is England (about 47% more).

And now let's see the lower range players. Will take players from 8001-st to 8300-th in the descending list of player values.

In [75]:
lower_paid_players=players_16.groupby(["short_name","nationality"])["value_eur"].sum().sort_values(ascending=False)[8001:8300]
lower_paid_players=pd.DataFrame(lower_paid_players,index=None).reset_index()
lower_paid_players.groupby("nationality")["nationality"].count().sort_values(ascending=False).head(10)
Out[75]:
nationality
England          23
France           21
Argentina        20
United States    18
Spain            15
Italy            15
Chile            15
Germany          12
Poland           11
Colombia         11
Name: nationality, dtype: int64

Here we can see that Spaniards are not largest in number.

From these last few statistics it seems that, in general, Spanish players are better paid than others, though the average price per player is not highest. Still, number of players in the set is very high and this can be the reason for this result.

In [76]:
nationality_and_value=players_top_20_nations[["nationality","value_eur"]]
fig = px.box(nationality_and_value, x="nationality", y="value_eur",title="Value per Nation")

fig.show()
In [77]:
players_top_20_nations[players_top_20_nations["nationality"]=="Spain"]["value_eur"].median()
Out[77]:
875000.0
In [78]:
players_top_20_nations[players_top_20_nations["nationality"]=="England"]["value_eur"].median()
Out[78]:
300000.0

Seeing the median values for Brazil(1.8M), Portugal(1.2M), Spain(875000) and England(300000) and having in mind the number of players from Spain (987), England (1450), Brazil(420) and Portugal(324), we can only conclude that Spanish players are much better paid than English ones. Brazil and Portugal are less in numbers and each have 1 very expensive player which rizes the average value per nationality.

Lets make a hypothesis:

$H0$ - there is not a substantial difference in the payment of English and Spanish players

$H1$ - there is a substantial difference in the payment of English and Spanish players

and we will use $\alpha_{c}$=1% since we have big dataset.

This we will test now and verify with the second dataset for 2020.

In [79]:
english_players_price=players_16[players_16["nationality"]=="England"]["value_eur"]
spanish_players_price=players_16[players_16["nationality"]=="Spain"]["value_eur"]
In [80]:
ttest_ind(english_players_price, spanish_players_price)
Out[80]:
Ttest_indResult(statistic=-11.46332634242886, pvalue=1.1461395477178186e-29)

With such a low pvalue we can reject the Null Hypothesis.

The final aim of this work is to create and train a model to predict the price of a player based on his capabilities. Before that we can see which features are most correlated to the value.

In [81]:
players_16.corr()
Out[81]:
age height_cm weight_kg overall potential value_eur international_reputation weak_foot skill_moves team_jersey_number contract_valid_until pace shooting passing dribbling defending physic gk_speed attacking_crossing attacking_finishing attacking_heading_accuracy attacking_short_passing attacking_volleys skill_dribbling skill_curve skill_fk_accuracy skill_long_passing skill_ball_control movement_acceleration movement_sprint_speed movement_agility movement_reactions movement_balance power_shot_power power_jumping power_stamina power_strength power_long_shots mentality_aggression mentality_interceptions mentality_positioning mentality_vision mentality_penalties defending_marking defending_standing_tackle defending_sliding_tackle goalkeeping_diving goalkeeping_handling goalkeeping_kicking goalkeeping_positioning goalkeeping_reflexes ls st rs lw lf cf rf rw lam cam ram lm lcm cm rcm rm lwb ldm cdm rdm rwb lb lcb cb rcb rb
age 1.000000 0.080412 0.218434 0.439736 -0.125627 0.079883 0.277710 0.077216 -0.018800 -0.228214 -0.151552 -0.164913 0.048660 0.062909 -0.026930 0.100032 0.069155 0.086077 0.062796 0.005349 0.080368 0.086728 0.061369 -0.051007 0.070079 0.122131 0.139071 0.029144 -0.224824 -0.218302 -0.073392 0.395858 -0.123581 0.088910 0.121634 0.023215 0.259827 0.076456 0.237996 0.167387 0.014522 0.160532 0.071897 0.095487 0.085014 0.068744 0.110753 0.118913 0.114074 0.126850 0.109512 0.022931 0.022931 0.022931 -0.003591 0.009109 0.009109 0.009109 -0.003591 0.013235 0.013235 0.013235 0.001210 0.050825 0.050825 0.050825 0.001210 0.046002 0.084582 0.084582 0.084582 0.046002 0.049920 0.091531 0.091531 0.091531 0.049920
height_cm 0.080412 1.000000 0.766455 0.060498 0.017680 0.015859 0.045723 -0.164617 -0.413659 -0.043021 -0.094536 -0.478165 -0.378375 -0.425457 -0.481723 -0.094135 -0.137255 0.337328 -0.458348 -0.358111 -0.028599 -0.341943 -0.344828 -0.475107 -0.421572 -0.384103 -0.319234 -0.394236 -0.515279 -0.435221 -0.599136 0.003806 -0.787934 -0.281572 -0.073728 -0.259871 0.531675 -0.379441 -0.045248 -0.038329 -0.414091 -0.307677 -0.320547 -0.044097 -0.052524 -0.071170 0.345080 0.344673 0.340714 0.346370 0.343593 -0.371797 -0.371797 -0.371797 -0.455301 -0.426990 -0.426990 -0.426990 -0.455301 -0.438802 -0.438802 -0.438802 -0.445813 -0.391435 -0.391435 -0.391435 -0.445813 -0.325603 -0.258504 -0.258504 -0.258504 -0.325603 -0.285420 -0.152169 -0.152169 -0.152169 -0.285420
weight_kg 0.218434 0.766455 1.000000 0.134976 0.007411 0.042073 0.093912 -0.129611 -0.363400 -0.084685 -0.078132 -0.451028 -0.320896 -0.379390 -0.434535 -0.094449 -0.100458 0.323379 -0.405305 -0.312001 -0.033478 -0.302296 -0.291449 -0.431251 -0.368087 -0.326886 -0.279506 -0.353959 -0.484357 -0.413137 -0.546209 0.071590 -0.697291 -0.220455 -0.024147 -0.231913 0.595008 -0.316240 -0.002919 -0.035367 -0.360297 -0.253481 -0.265101 -0.051303 -0.063893 -0.081469 0.337671 0.339249 0.334518 0.340144 0.334678 -0.325837 -0.325837 -0.325837 -0.407746 -0.379890 -0.379890 -0.379890 -0.407746 -0.392627 -0.392627 -0.392627 -0.400793 -0.350573 -0.350573 -0.350573 -0.400793 -0.298411 -0.233983 -0.233983 -0.233983 -0.298411 -0.264230 -0.137809 -0.137809 -0.137809 -0.264230
overall 0.439736 0.060498 0.134976 1.000000 0.781817 0.598158 0.491618 0.205962 0.243675 -0.179027 -0.013182 0.144062 0.314795 0.356585 0.300196 0.250692 0.251938 -0.050719 0.337289 0.258967 0.285335 0.462311 0.313773 0.301068 0.354916 0.329594 0.449014 0.406070 0.157273 0.176556 0.211860 0.792814 0.037695 0.376679 0.237238 0.303921 0.292941 0.341751 0.391474 0.306587 0.286826 0.454645 0.288142 0.199397 0.224607 0.191321 -0.013305 -0.008285 -0.011916 -0.004463 -0.013170 0.321237 0.321237 0.321237 0.314071 0.324193 0.324193 0.324193 0.314071 0.328978 0.328978 0.328978 0.319067 0.355663 0.355663 0.355663 0.319067 0.314825 0.331386 0.331386 0.331386 0.314825 0.302586 0.287742 0.287742 0.287742 0.302586
potential -0.125627 0.017680 0.007411 0.781817 1.000000 0.576793 0.380552 0.178361 0.255135 -0.022922 0.085902 0.213147 0.282428 0.314101 0.308350 0.177525 0.182002 -0.080463 0.289771 0.255918 0.225003 0.419149 0.279155 0.328817 0.316465 0.261308 0.374957 0.391883 0.265961 0.278954 0.246533 0.590323 0.109251 0.322904 0.160963 0.246595 0.128617 0.296538 0.241544 0.199210 0.272169 0.380165 0.251420 0.129611 0.162444 0.139049 -0.054610 -0.052694 -0.054882 -0.053324 -0.053684 0.297491 0.297491 0.297491 0.306260 0.310361 0.310361 0.310361 0.306260 0.313618 0.313618 0.313618 0.306790 0.317066 0.317066 0.317066 0.306790 0.270101 0.267885 0.267885 0.267885 0.270101 0.255058 0.217449 0.217449 0.217449 0.255058
value_eur 0.079883 0.015859 0.042073 0.598158 0.576793 1.000000 0.676484 0.140724 0.217407 -0.065684 0.092588 0.106889 0.212288 0.214805 0.194875 0.086131 0.107833 -0.015566 0.195654 0.194837 0.135334 0.272554 0.229246 0.204294 0.226008 0.204509 0.253297 0.245157 0.152016 0.153989 0.159247 0.471799 0.065007 0.217239 0.110937 0.164818 0.101879 0.216411 0.155696 0.114374 0.200393 0.294718 0.201538 0.043116 0.075388 0.056511 -0.011160 -0.010169 -0.012252 -0.011187 -0.011180 0.206180 0.206180 0.206180 0.207746 0.213569 0.213569 0.213569 0.207746 0.214946 0.214946 0.214946 0.205865 0.214490 0.214490 0.214490 0.205865 0.164308 0.164751 0.164751 0.164751 0.164308 0.150920 0.123135 0.123135 0.123135 0.150920
international_reputation 0.277710 0.045723 0.093912 0.491618 0.380552 0.676484 1.000000 0.110302 0.138444 -0.063367 0.002140 0.017002 0.160887 0.163882 0.123161 0.088488 0.080794 0.005066 0.142636 0.130259 0.124098 0.198796 0.192290 0.121119 0.173613 0.165791 0.197237 0.164953 0.016482 0.014446 0.056832 0.384748 0.004914 0.172561 0.093846 0.068915 0.112206 0.161859 0.145597 0.108258 0.135066 0.228296 0.181447 0.050547 0.073716 0.061860 0.008567 0.008862 0.007609 0.010341 0.007519 0.148768 0.148768 0.148768 0.139487 0.148277 0.148277 0.148277 0.139487 0.149761 0.149761 0.149761 0.138593 0.159101 0.159101 0.159101 0.138593 0.122380 0.134257 0.134257 0.134257 0.122380 0.115759 0.111357 0.111357 0.111357 0.115759
weak_foot 0.077216 -0.164617 -0.129611 0.205962 0.178361 0.140724 0.110302 1.000000 0.326654 -0.043307 -0.037653 0.269767 0.361478 0.332389 0.337112 0.088885 0.215682 -0.233476 0.301782 0.342898 0.192889 0.306543 0.346696 0.333929 0.335910 0.321467 0.273411 0.338561 0.216583 0.205218 0.257929 0.175951 0.204510 0.314252 0.049819 0.204976 -0.020808 0.347918 0.138597 0.056026 0.335953 0.302370 0.316899 0.025367 0.050568 0.030081 -0.229314 -0.225646 -0.227129 -0.227467 -0.231161 0.338631 0.338631 0.338631 0.344032 0.346500 0.346500 0.346500 0.344032 0.345704 0.345704 0.345704 0.335959 0.321143 0.321143 0.321143 0.335959 0.239683 0.223933 0.223933 0.223933 0.239683 0.214493 0.161987 0.161987 0.161987 0.214493
skill_moves -0.018800 -0.413659 -0.363400 0.243675 0.255135 0.217407 0.138444 0.326654 1.000000 0.005619 0.025072 0.689423 0.748240 0.702009 0.756512 0.184926 0.492610 -0.605735 0.641123 0.704324 0.414778 0.603214 0.693460 0.744145 0.688348 0.643231 0.502428 0.705934 0.559510 0.524851 0.578740 0.194143 0.465349 0.633273 0.006224 0.428807 -0.116498 0.675824 0.240455 0.065951 0.705360 0.514639 0.646602 0.042260 0.097889 0.061889 -0.603506 -0.598528 -0.598083 -0.599330 -0.602399 0.736327 0.736327 0.736327 0.757659 0.753949 0.753949 0.753949 0.757659 0.749065 0.749065 0.749065 0.741494 0.684234 0.684234 0.684234 0.741494 0.531699 0.478510 0.478510 0.478510 0.531699 0.477996 0.352840 0.352840 0.352840 0.477996
team_jersey_number -0.228214 -0.043021 -0.084685 -0.179027 -0.022922 -0.065684 -0.063367 -0.043307 0.005619 1.000000 0.095653 0.000264 -0.012507 -0.053494 -0.019520 -0.106243 -0.085053 0.009708 -0.041065 0.011293 -0.062259 -0.064427 0.000833 -0.001282 -0.028309 -0.042253 -0.085934 -0.042484 0.026925 0.015438 0.000150 -0.138510 0.033491 -0.032472 -0.080415 -0.088479 -0.122524 -0.014596 -0.124978 -0.130022 -0.008546 -0.074602 -0.005210 -0.103717 -0.102330 -0.090515 -0.000528 -0.006068 -0.005026 -0.006368 -0.001365 -0.031245 -0.031245 -0.031245 -0.026383 -0.028186 -0.028186 -0.028186 -0.026383 -0.033223 -0.033223 -0.033223 -0.034475 -0.058140 -0.058140 -0.058140 -0.034475 -0.078242 -0.092148 -0.092148 -0.092148 -0.078242 -0.083102 -0.098735 -0.098735 -0.098735 -0.083102
contract_valid_until -0.151552 -0.094536 -0.078132 -0.013182 0.085902 0.092588 0.002140 -0.037653 0.025072 0.095653 1.000000 0.023659 -0.008545 -0.004539 0.010733 -0.003745 -0.012795 -0.002950 -0.006763 0.001818 0.001287 0.010506 -0.009427 0.024451 -0.012891 -0.031438 0.000777 -0.000693 0.045708 0.039327 0.006231 -0.006916 0.025078 -0.013704 0.000822 0.007775 -0.034861 -0.005254 -0.040480 -0.018834 0.003121 -0.017567 -0.004697 0.003763 0.001320 0.009542 -0.010811 -0.011434 -0.012459 -0.013338 -0.010358 -0.000028 -0.000028 -0.000028 0.004863 0.003806 0.003806 0.003806 0.004863 0.003808 0.003808 0.003808 0.005199 0.000444 0.000444 0.000444 0.005199 0.001424 -0.003559 -0.003559 -0.003559 0.001424 0.001553 -0.004776 -0.004776 -0.004776 0.001553
pace -0.164913 -0.478165 -0.451028 0.144062 0.213147 0.106889 0.017002 0.269767 0.689423 0.000264 0.023659 1.000000 0.800930 0.845809 0.913649 0.528046 0.787867 -0.880618 0.774195 0.697979 0.662325 0.705381 0.683401 0.855954 0.722824 0.646110 0.592139 0.823366 0.796440 0.801431 0.670023 0.104890 0.555840 0.713885 0.159667 0.691661 -0.042270 0.704026 0.471350 0.333314 0.777716 0.435799 0.662849 0.373159 0.414313 0.395538 -0.873950 -0.868274 -0.865116 -0.868614 -0.873302 0.895389 0.895389 0.895389 0.916727 0.902720 0.902720 0.902720 0.916727 0.897215 0.897215 0.897215 0.918541 0.868593 0.868593 0.868593 0.918541 0.832935 0.764888 0.764888 0.764888 0.832935 0.800295 0.684496 0.684496 0.684496 0.800295
shooting 0.048660 -0.378375 -0.320896 0.314795 0.282428 0.212288 0.160887 0.361478 0.748240 -0.012507 -0.008545 0.800930 1.000000 0.874241 0.912135 0.324036 0.705047 -0.763411 0.770076 0.944084 0.647219 0.765730 0.900608 0.887695 0.833123 0.795811 0.640998 0.874899 0.571517 0.560447 0.598910 0.283622 0.456179 0.893741 0.082872 0.590598 0.026450 0.927343 0.419787 0.168501 0.910771 0.632900 0.862874 0.141720 0.201477 0.154064 -0.758078 -0.752263 -0.749882 -0.753777 -0.757140 0.958163 0.958163 0.958163 0.938259 0.951668 0.951668 0.951668 0.938259 0.936173 0.936173 0.936173 0.918355 0.876037 0.876037 0.876037 0.918355 0.698758 0.656536 0.656536 0.656536 0.698758 0.643723 0.527674 0.527674 0.527674 0.643723
passing 0.062909 -0.425457 -0.379390 0.356585 0.314101 0.214805 0.163882 0.332389 0.702009 -0.053494 -0.004539 0.845809 0.874241 1.000000 0.954585 0.628087 0.815567 -0.849273 0.901292 0.745060 0.694386 0.916255 0.770690 0.900838 0.862822 0.830440 0.848701 0.926499 0.559334 0.544179 0.594130 0.309449 0.501967 0.820885 0.103986 0.696999 0.036644 0.831794 0.590796 0.485877 0.835317 0.657614 0.752145 0.471315 0.526870 0.488785 -0.842429 -0.836956 -0.834337 -0.837036 -0.841635 0.940656 0.940656 0.940656 0.965312 0.960350 0.960350 0.960350 0.965312 0.975713 0.975713 0.975713 0.975841 0.987252 0.987252 0.987252 0.975841 0.908033 0.883237 0.883237 0.883237 0.908033 0.869601 0.770362 0.770362 0.770362 0.869601
dribbling -0.026930 -0.481723 -0.434535 0.300196 0.308350 0.194875 0.123161 0.337112 0.756512 -0.019520 0.010733 0.913649 0.912135 0.954585 1.000000 0.524436 0.792629 -0.867221 0.864360 0.808459 0.683927 0.845044 0.806689 0.965906 0.846774 0.784268 0.735686 0.947548 0.663758 0.643874 0.680627 0.265283 0.564826 0.821653 0.116811 0.684340 -0.024431 0.841241 0.514197 0.356018 0.880288 0.606899 0.776331 0.355289 0.408721 0.375334 -0.860783 -0.855039 -0.852224 -0.855846 -0.859972 0.969502 0.969502 0.969502 0.992113 0.986707 0.986707 0.986707 0.992113 0.988354 0.988354 0.988354 0.990349 0.962021 0.962021 0.962021 0.990349 0.860507 0.812096 0.812096 0.812096 0.860507 0.814175 0.695236 0.695236 0.695236 0.814175
defending 0.100032 -0.094135 -0.094449 0.250692 0.177525 0.086131 0.088488 0.088885 0.184926 -0.106243 -0.003745 0.528046 0.324036 0.628087 0.524436 1.000000 0.793093 -0.675525 0.522255 0.134424 0.693960 0.581427 0.218502 0.415491 0.383564 0.392132 0.604133 0.513999 0.176681 0.201683 0.139224 0.182920 0.145764 0.419787 0.217069 0.598906 0.306040 0.311583 0.759105 0.900588 0.297375 0.182386 0.262622 0.950341 0.962533 0.946758 -0.668346 -0.666287 -0.662787 -0.662892 -0.669583 0.526746 0.526746 0.526746 0.523097 0.516990 0.516990 0.516990 0.523097 0.543815 0.543815 0.543815 0.571736 0.683010 0.683010 0.683010 0.571736 0.875960 0.911355 0.911355 0.911355 0.875960 0.914383 0.966683 0.966683 0.966683 0.914383
physic 0.069155 -0.137255 -0.100458 0.251938 0.182002 0.107833 0.080794 0.215682 0.492610 -0.085053 -0.012795 0.787867 0.705047 0.815567 0.792629 0.793093 1.000000 -0.894652 0.666157 0.546773 0.874765 0.712310 0.574340 0.692573 0.607215 0.587034 0.629375 0.757520 0.387746 0.422150 0.330229 0.193011 0.224638 0.710323 0.237934 0.757023 0.415120 0.616825 0.769158 0.613573 0.645750 0.348580 0.595183 0.644740 0.685592 0.643789 -0.886312 -0.881632 -0.879163 -0.879946 -0.887810 0.848474 0.848474 0.848474 0.808134 0.816910 0.816910 0.816910 0.808134 0.817815 0.817815 0.817815 0.832759 0.874412 0.874412 0.874412 0.832759 0.915032 0.920885 0.920885 0.920885 0.915032 0.915544 0.914284 0.914284 0.914284 0.915544
gk_speed 0.086077 0.337328 0.323379 -0.050719 -0.080463 -0.015566 0.005066 -0.233476 -0.605735 0.009708 -0.002950 -0.880618 -0.763411 -0.849273 -0.867221 -0.675525 -0.894652 1.000000 -0.708400 -0.627433 -0.790570 -0.699086 -0.629532 -0.774641 -0.661731 -0.629654 -0.597333 -0.790668 -0.463939 -0.465746 -0.435507 -0.028167 -0.384146 -0.699581 -0.095970 -0.645944 -0.088828 -0.654188 -0.568473 -0.453756 -0.705665 -0.368787 -0.644618 -0.514756 -0.556996 -0.526258 0.959659 0.951587 0.950879 0.950258 0.958423 -0.882866 -0.882866 -0.882866 -0.870797 -0.870610 -0.870610 -0.870610 -0.870797 -0.871464 -0.871464 -0.871464 -0.883713 -0.887769 -0.887769 -0.887769 -0.883713 -0.881761 -0.857812 -0.857812 -0.857812 -0.881761 -0.866853 -0.813820 -0.813820 -0.813820 -0.866853
attacking_crossing 0.062796 -0.458348 -0.405305 0.337289 0.289771 0.195654 0.142636 0.301782 0.641123 -0.041065 -0.006763 0.774195 0.770076 0.901292 0.864360 0.522255 0.666157 -0.708400 1.000000 0.675164 0.545065 0.800872 0.702745 0.850750 0.831344 0.773463 0.751791 0.834426 0.589846 0.573909 0.610416 0.296189 0.520314 0.721327 0.087195 0.633683 -0.043699 0.759907 0.486414 0.413056 0.781636 0.590592 0.663582 0.405365 0.448174 0.427599 -0.706749 -0.702701 -0.699935 -0.703763 -0.705759 0.822018 0.822018 0.822018 0.875131 0.851572 0.851572 0.851572 0.875131 0.862944 0.862944 0.862944 0.881982 0.859988 0.859988 0.859988 0.881982 0.808197 0.751275 0.751275 0.751275 0.808197 0.764308 0.640622 0.640622 0.640622 0.764308
attacking_finishing 0.005349 -0.358111 -0.312001 0.258967 0.255918 0.194837 0.130259 0.342898 0.704324 0.011293 0.001818 0.697979 0.944084 0.745060 0.808459 0.134424 0.546773 -0.627433 0.675164 1.000000 0.526821 0.653716 0.863921 0.819004 0.754751 0.710470 0.517743 0.779758 0.544159 0.528919 0.570481 0.248193 0.429667 0.783201 0.043537 0.477330 -0.034551 0.854079 0.267060 0.000603 0.870155 0.595582 0.813355 -0.029025 0.026011 -0.020106 -0.627670 -0.622927 -0.619716 -0.626061 -0.626086 0.859337 0.859337 0.859337 0.838406 0.852345 0.852345 0.852345 0.838406 0.830024 0.830024 0.830024 0.807571 0.739998 0.739998 0.739998 0.807571 0.528117 0.477998 0.477998 0.477998 0.528117 0.466539 0.341281 0.341281 0.341281 0.466539
attacking_heading_accuracy 0.080368 -0.028599 -0.033478 0.285335 0.225003 0.135334 0.124098 0.192889 0.414778 -0.062259 0.001287 0.662325 0.647219 0.694386 0.683927 0.693960 0.874765 -0.790570 0.545065 0.526821 1.000000 0.623552 0.544204 0.598998 0.514431 0.491310 0.523356 0.675608 0.276705 0.320428 0.221345 0.218165 0.116292 0.657084 0.254868 0.592849 0.382820 0.550307 0.659538 0.512355 0.579896 0.269583 0.571513 0.553592 0.582708 0.543606 -0.787212 -0.784757 -0.779547 -0.782220 -0.787717 0.775686 0.775686 0.775686 0.702986 0.723562 0.723562 0.723562 0.702986 0.714178 0.714178 0.714178 0.719612 0.758542 0.758542 0.758542 0.719612 0.781133 0.794573 0.794573 0.794573 0.781133 0.789995 0.811381 0.811381 0.811381 0.789995
attacking_short_passing 0.086728 -0.341943 -0.302296 0.462311 0.419149 0.272554 0.198796 0.306543 0.603214 -0.064427 0.010506 0.705381 0.765730 0.916255 0.845044 0.581427 0.712310 -0.699086 0.800872 0.653716 0.623552 1.000000 0.681397 0.811591 0.767307 0.737667 0.884123 0.873523 0.473710 0.463438 0.521574 0.409666 0.429366 0.753900 0.116423 0.634016 0.082916 0.752893 0.571531 0.487002 0.735982 0.640776 0.663619 0.450958 0.505908 0.462825 -0.694399 -0.689938 -0.686612 -0.689461 -0.693534 0.829176 0.829176 0.829176 0.853626 0.852871 0.852871 0.852871 0.853626 0.875312 0.875312 0.875312 0.867623 0.900025 0.900025 0.900025 0.867623 0.817747 0.813068 0.813068 0.813068 0.817747 0.783080 0.700933 0.700933 0.700933 0.783080
attacking_volleys 0.061369 -0.344828 -0.291449 0.313773 0.279155 0.229246 0.192290 0.346696 0.693460 0.000833 -0.009427 0.683401 0.900608 0.770690 0.806689 0.218502 0.574340 -0.629532 0.702745 0.863921 0.544204 0.681397 1.000000 0.805800 0.793098 0.745444 0.566144 0.782048 0.513073 0.495831 0.559215 0.293150 0.427023 0.797948 0.066621 0.499465 -0.007675 0.846999 0.339563 0.098818 0.836505 0.602356 0.795701 0.062621 0.118518 0.076556 -0.629848 -0.625313 -0.621765 -0.627386 -0.628249 0.843690 0.843690 0.843690 0.829032 0.840686 0.840686 0.840686 0.829032 0.826355 0.826355 0.826355 0.807050 0.760627 0.760627 0.760627 0.807050 0.577657 0.535637 0.535637 0.535637 0.577657 0.521969 0.407616 0.407616 0.407616 0.521969
skill_dribbling -0.051007 -0.475107 -0.431251 0.301068 0.328817 0.204294 0.121119 0.333929 0.744145 -0.001282 0.024451 0.855954 0.887695 0.900838 0.965906 0.415491 0.692573 -0.774641 0.850750 0.819004 0.598998 0.811591 0.805800 1.000000 0.838454 0.768556 0.701822 0.918433 0.668251 0.645656 0.673653 0.270693 0.555582 0.794862 0.078759 0.631916 -0.063770 0.838788 0.431736 0.270540 0.878515 0.623687 0.760631 0.262768 0.315544 0.284408 -0.772031 -0.766865 -0.763112 -0.769358 -0.771108 0.919107 0.919107 0.919107 0.950743 0.943775 0.943775 0.943775 0.950743 0.942851 0.942851 0.942851 0.943085 0.898746 0.898746 0.898746 0.943085 0.772177 0.715617 0.715617 0.715617 0.772177 0.718024 0.586209 0.586209 0.586209 0.718024
skill_curve 0.070079 -0.421572 -0.368087 0.354916 0.316465 0.226008 0.173613 0.335910 0.688348 -0.028309 -0.012891 0.722824 0.833123 0.862822 0.846774 0.383564 0.607215 -0.661731 0.831344 0.754751 0.514431 0.767307 0.793098 0.838454 1.000000 0.850194 0.699538 0.825819 0.550957 0.529230 0.606768 0.316768 0.495136 0.770820 0.057323 0.564913 -0.052682 0.826953 0.411241 0.282946 0.807997 0.635218 0.746328 0.246658 0.301985 0.268331 -0.661427 -0.656669 -0.654681 -0.660964 -0.660563 0.829595 0.829595 0.829595 0.857413 0.852536 0.852536 0.852536 0.857413 0.856633 0.856633 0.856633 0.850916 0.827431 0.827431 0.827431 0.850916 0.702371 0.659306 0.659306 0.659306 0.702371 0.651881 0.530464 0.530464 0.530464 0.651881
skill_fk_accuracy 0.122131 -0.384103 -0.326886 0.329594 0.261308 0.204509 0.165791 0.321467 0.643231 -0.042253 -0.031438 0.646110 0.795811 0.830440 0.784268 0.392132 0.587034 -0.629654 0.773463 0.710470 0.491310 0.737667 0.745444 0.768556 0.850194 1.000000 0.693794 0.770411 0.455804 0.427267 0.531066 0.299935 0.445329 0.749776 0.037720 0.526272 -0.028095 0.804177 0.418455 0.305571 0.744764 0.618790 0.738011 0.262319 0.318619 0.280936 -0.628741 -0.623520 -0.620866 -0.627084 -0.626592 0.780259 0.780259 0.780259 0.800120 0.799696 0.799696 0.799696 0.800120 0.807038 0.807038 0.807038 0.795987 0.791672 0.791672 0.791672 0.795987 0.672190 0.645073 0.645073 0.645073 0.672190 0.627274 0.524233 0.524233 0.524233 0.627274
skill_long_passing 0.139071 -0.319234 -0.279506 0.449014 0.374957 0.253297 0.197237 0.273411 0.502428 -0.085934 0.000777 0.592139 0.640998 0.848701 0.735686 0.604133 0.629375 -0.597333 0.751791 0.517743 0.523356 0.884123 0.566144 0.701822 0.699538 0.693794 1.000000 0.766071 0.385430 0.372229 0.456365 0.388877 0.387601 0.659658 0.109985 0.583382 0.067763 0.667167 0.556592 0.547917 0.607318 0.611578 0.550824 0.504891 0.550589 0.517332 -0.593282 -0.588155 -0.585491 -0.588310 -0.591246 0.704655 0.704655 0.704655 0.741146 0.735400 0.735400 0.735400 0.741146 0.768470 0.768470 0.768470 0.765193 0.823421 0.823421 0.823421 0.765193 0.769808 0.781576 0.781576 0.781576 0.769808 0.744346 0.677501 0.677501 0.677501 0.744346
skill_ball_control 0.029144 -0.394236 -0.353959 0.406070 0.391883 0.245157 0.164953 0.338561 0.705934 -0.042484 -0.000693 0.823366 0.874899 0.926499 0.947548 0.513999 0.757520 -0.790668 0.834426 0.779758 0.675608 0.873523 0.782048 0.918433 0.825819 0.770411 0.766071 1.000000 0.585556 0.573960 0.609808 0.355175 0.486774 0.822409 0.118261 0.655850 0.042078 0.827870 0.530494 0.376468 0.843712 0.628627 0.758144 0.357393 0.410996 0.371727 -0.784465 -0.780271 -0.776465 -0.779936 -0.784129 0.926808 0.926808 0.926808 0.943087 0.943181 0.943181 0.943181 0.943087 0.947908 0.947908 0.947908 0.943578 0.933271 0.933271 0.933271 0.943578 0.824830 0.792925 0.792925 0.792925 0.824830 0.780885 0.675669 0.675669 0.675669 0.780885
movement_acceleration -0.224824 -0.515279 -0.484357 0.157273 0.265961 0.152016 0.016482 0.216583 0.559510 0.026925 0.045708 0.796440 0.571517 0.559334 0.663758 0.176681 0.387746 -0.463939 0.589846 0.544159 0.276705 0.473710 0.513073 0.668251 0.550957 0.455804 0.385430 0.585556 1.000000 0.865255 0.734950 0.130906 0.603083 0.478476 0.168976 0.508321 -0.222655 0.525611 0.183487 0.075532 0.601232 0.366858 0.459133 0.080902 0.106630 0.110050 -0.493847 -0.491974 -0.488022 -0.493843 -0.493340 0.604166 0.604166 0.604166 0.661794 0.635606 0.635606 0.635606 0.661794 0.626725 0.626725 0.626725 0.649924 0.554812 0.554812 0.554812 0.649924 0.493498 0.398022 0.398022 0.398022 0.493498 0.450753 0.298822 0.298822 0.298822 0.450753
movement_sprint_speed -0.218302 -0.435221 -0.413137 0.176556 0.278954 0.153989 0.014446 0.205218 0.524851 0.015438 0.039327 0.801431 0.560447 0.544179 0.643874 0.201683 0.422150 -0.465746 0.573909 0.528919 0.320428 0.463438 0.495831 0.645656 0.529230 0.427267 0.372229 0.573960 0.865255 1.000000 0.681146 0.135018 0.528762 0.486232 0.186220 0.519681 -0.139247 0.505146 0.216984 0.094755 0.585666 0.333093 0.448614 0.105869 0.131338 0.132122 -0.495886 -0.495793 -0.490757 -0.497843 -0.496280 0.603702 0.603702 0.603702 0.647820 0.624833 0.624833 0.624833 0.647820 0.612193 0.612193 0.612193 0.638277 0.547184 0.547184 0.547184 0.638277 0.501434 0.408964 0.408964 0.408964 0.501434 0.463781 0.325201 0.325201 0.325201 0.463781
movement_agility -0.073392 -0.599136 -0.546209 0.211860 0.246533 0.159247 0.056832 0.257929 0.578740 0.000150 0.006231 0.670023 0.598910 0.594130 0.680627 0.139224 0.330229 -0.435507 0.610416 0.570481 0.221345 0.521574 0.559215 0.673653 0.606768 0.531066 0.456365 0.609808 0.734950 0.681146 1.000000 0.208564 0.682670 0.491919 0.182293 0.459095 -0.295513 0.576066 0.179222 0.065143 0.618571 0.485716 0.494055 0.038587 0.067829 0.067121 -0.442482 -0.440033 -0.439042 -0.442835 -0.441984 0.594003 0.594003 0.594003 0.661655 0.636283 0.636283 0.636283 0.661655 0.640666 0.640666 0.640666 0.643769 0.570597 0.570597 0.570597 0.643769 0.463525 0.385108 0.385108 0.385108 0.463525 0.411821 0.259553 0.259553 0.259553 0.411821
movement_reactions 0.395858 0.003806 0.071590 0.792814 0.590323 0.471799 0.384748 0.175951 0.194143 -0.138510 -0.006916 0.104890 0.283622 0.309449 0.265283 0.182920 0.193011 -0.028167 0.296189 0.248193 0.218165 0.409666 0.293150 0.270693 0.316768 0.299935 0.388877 0.355175 0.130906 0.135018 0.208564 1.000000 0.074565 0.320461 0.203560 0.272519 0.214872 0.317900 0.336892 0.249985 0.297775 0.429489 0.257361 0.139241 0.160398 0.133325 -0.000527 0.002699 -0.000727 0.005807 -0.001361 0.284001 0.284001 0.284001 0.281018 0.292871 0.292871 0.292871 0.281018 0.295602 0.295602 0.295602 0.282749 0.313722 0.313722 0.313722 0.282749 0.260861 0.273106 0.273106 0.273106 0.260861 0.245538 0.222267 0.222267 0.222267 0.245538
movement_balance -0.123581 -0.787934 -0.697291 0.037695 0.109251 0.065007 0.004914 0.204510 0.465349 0.033491 0.025078 0.555840 0.456179 0.501967 0.564826 0.145764 0.224638 -0.384146 0.520314 0.429667 0.116292 0.429366 0.427023 0.555582 0.495136 0.445329 0.387601 0.486774 0.603083 0.528762 0.682670 0.074565 1.000000 0.363177 0.143657 0.353096 -0.446657 0.451306 0.115319 0.083790 0.487201 0.376408 0.386284 0.079182 0.097653 0.111921 -0.401418 -0.399526 -0.395503 -0.401498 -0.399564 0.459218 0.459218 0.459218 0.537243 0.510825 0.510825 0.510825 0.537243 0.521549 0.521549 0.521549 0.528261 0.473680 0.473680 0.473680 0.528261 0.400874 0.332768 0.332768 0.332768 0.400874 0.358966 0.222051 0.222051 0.222051 0.358966
power_shot_power 0.088910 -0.281572 -0.220455 0.376679 0.322904 0.217239 0.172561 0.314252 0.633273 -0.032472 -0.013704 0.713885 0.893741 0.820885 0.821653 0.419787 0.710323 -0.699581 0.721327 0.783201 0.657084 0.753900 0.797948 0.794862 0.770820 0.749776 0.659658 0.822409 0.478476 0.486232 0.491919 0.320461 0.363177 1.000000 0.115005 0.589868 0.135769 0.862322 0.508822 0.292827 0.792400 0.567777 0.772706 0.266954 0.320677 0.276584 -0.697839 -0.693702 -0.690823 -0.694882 -0.696986 0.876177 0.876177 0.876177 0.844244 0.862492 0.862492 0.862492 0.844244 0.850102 0.850102 0.850102 0.836326 0.828000 0.828000 0.828000 0.836326 0.705215 0.684018 0.684018 0.684018 0.705215 0.665902 0.585250 0.585250 0.585250 0.665902
power_jumping 0.121634 -0.073728 -0.024147 0.237238 0.160963 0.110937 0.093846 0.049819 0.006224 -0.080415 0.000822 0.159667 0.082872 0.103986 0.116811 0.217069 0.237934 -0.095970 0.087195 0.043537 0.254868 0.116423 0.066621 0.078759 0.057323 0.037720 0.109985 0.118261 0.168976 0.186220 0.182293 0.203560 0.143657 0.115005 1.000000 0.242498 0.172379 0.069583 0.264087 0.210933 0.080256 0.034678 0.070303 0.189044 0.185032 0.192103 -0.101529 -0.101283 -0.099919 -0.099698 -0.102472 0.139095 0.139095 0.139095 0.118867 0.120011 0.120011 0.120011 0.118867 0.115986 0.115986 0.115986 0.126938 0.140109 0.140109 0.140109 0.126938 0.196337 0.196919 0.196919 0.196919 0.196337 0.209495 0.236918 0.236918 0.236918 0.209495
power_stamina 0.023215 -0.259871 -0.231913 0.303921 0.246595 0.164818 0.068915 0.204976 0.428807 -0.088479 0.007775 0.691661 0.590598 0.696999 0.684340 0.598906 0.757023 -0.645944 0.633683 0.477330 0.592849 0.634016 0.499465 0.631916 0.564913 0.526272 0.583382 0.655850 0.508321 0.519681 0.459095 0.272519 0.353096 0.589868 0.242498 1.000000 0.191492 0.564325 0.583810 0.501858 0.591054 0.397458 0.484088 0.496106 0.529711 0.503195 -0.642126 -0.639228 -0.636662 -0.638767 -0.643495 0.688676 0.688676 0.688676 0.695161 0.689726 0.689726 0.689726 0.695161 0.695555 0.695555 0.695555 0.720161 0.737206 0.737206 0.737206 0.720161 0.764014 0.736461 0.736461 0.736461 0.764014 0.748729 0.685520 0.685520 0.685520 0.748729
power_strength 0.259827 0.531675 0.595008 0.292941 0.128617 0.101879 0.112206 -0.020808 -0.116498 -0.122524 -0.034861 -0.042270 0.026450 0.036644 -0.024431 0.306040 0.415120 -0.088828 -0.043699 -0.034551 0.382820 0.082916 -0.007675 -0.063770 -0.052682 -0.028095 0.067763 0.042078 -0.222655 -0.139247 -0.295513 0.214872 -0.446657 0.135769 0.172379 0.191492 1.000000 0.003175 0.391389 0.301374 -0.020422 -0.053100 0.021208 0.286243 0.295564 0.255583 -0.084551 -0.081549 -0.084190 -0.078650 -0.087483 0.098058 0.098058 0.098058 0.004797 0.032616 0.032616 0.032616 0.004797 0.023525 0.023525 0.023525 0.024305 0.093435 0.093435 0.093435 0.024305 0.168889 0.233944 0.233944 0.233944 0.168889 0.201371 0.323689 0.323689 0.323689 0.201371
power_long_shots 0.076456 -0.379441 -0.316240 0.341751 0.296538 0.216411 0.161859 0.347918 0.675824 -0.014596 -0.005254 0.704026 0.927343 0.831794 0.841241 0.311583 0.616825 -0.654188 0.759907 0.854079 0.550307 0.752893 0.846999 0.838788 0.826953 0.804177 0.667167 0.827870 0.525611 0.505146 0.576066 0.317900 0.451306 0.862322 0.069583 0.564325 0.003175 1.000000 0.400693 0.203746 0.845436 0.647124 0.789812 0.159072 0.216314 0.171608 -0.654598 -0.650759 -0.646422 -0.651769 -0.653411 0.867660 0.867660 0.867660 0.864848 0.874767 0.874767 0.874767 0.864848 0.868562 0.868562 0.868562 0.846400 0.822542 0.822542 0.822542 0.846400 0.652589 0.617293 0.617293 0.617293 0.652589 0.598755 0.484314 0.484314 0.484314 0.598755
mentality_aggression 0.237996 -0.045248 -0.002919 0.391474 0.241544 0.155696 0.145597 0.138597 0.240455 -0.124978 -0.040480 0.471350 0.419787 0.590796 0.514197 0.759105 0.769158 -0.568473 0.486414 0.267060 0.659538 0.571531 0.339563 0.431736 0.411241 0.418455 0.556592 0.530494 0.183487 0.216984 0.179222 0.336892 0.115319 0.508822 0.264087 0.583810 0.391389 0.400693 1.000000 0.695470 0.390661 0.276910 0.344994 0.680253 0.709337 0.678957 -0.558962 -0.555333 -0.553184 -0.551825 -0.560739 0.554021 0.554021 0.554021 0.526201 0.533836 0.533836 0.533836 0.526201 0.545512 0.545512 0.545512 0.558113 0.639390 0.639390 0.639390 0.558113 0.734497 0.775185 0.775185 0.775185 0.734497 0.752965 0.800709 0.800709 0.800709 0.752965
mentality_interceptions 0.167387 -0.038329 -0.035367 0.306587 0.199210 0.114374 0.108258 0.056026 0.065951 -0.130022 -0.018834 0.333314 0.168501 0.485877 0.356018 0.900588 0.613573 -0.453756 0.413056 0.000603 0.512355 0.487002 0.098818 0.270540 0.282946 0.305571 0.547917 0.376468 0.075532 0.094755 0.065143 0.249985 0.083790 0.292827 0.210933 0.501858 0.301374 0.203746 0.695470 1.000000 0.168677 0.160827 0.127833 0.876480 0.890864 0.873848 -0.453764 -0.453750 -0.448732 -0.449937 -0.455025 0.346869 0.346869 0.346869 0.354886 0.347690 0.347690 0.347690 0.354886 0.380442 0.380442 0.380442 0.404170 0.530829 0.530829 0.530829 0.404170 0.728475 0.776615 0.776615 0.776615 0.728475 0.767959 0.829185 0.829185 0.829185 0.767959
mentality_positioning 0.014522 -0.414091 -0.360297 0.286826 0.272169 0.200393 0.135066 0.335953 0.705360 -0.008546 0.003121 0.777716 0.910771 0.835317 0.880288 0.297375 0.645750 -0.705665 0.781636 0.870155 0.579896 0.735982 0.836505 0.878515 0.807997 0.744764 0.607318 0.843712 0.601232 0.585666 0.618571 0.297775 0.487201 0.792400 0.080256 0.591054 -0.020422 0.845436 0.390661 0.168677 1.000000 0.631142 0.786006 0.134477 0.192662 0.153813 -0.706378 -0.700799 -0.698006 -0.703979 -0.705836 0.903281 0.903281 0.903281 0.904368 0.911966 0.911966 0.911966 0.904368 0.898125 0.898125 0.898125 0.887381 0.835864 0.835864 0.835864 0.887381 0.668715 0.616742 0.616742 0.616742 0.668715 0.612659 0.486627 0.486627 0.486627 0.612659
mentality_vision 0.160532 -0.307677 -0.253481 0.454645 0.380165 0.294718 0.228296 0.302370 0.514639 -0.074602 -0.017567 0.435799 0.632900 0.657614 0.606899 0.182386 0.348580 -0.368787 0.590592 0.595582 0.269583 0.640776 0.602356 0.623687 0.635218 0.618790 0.611578 0.628627 0.366858 0.333093 0.485716 0.429489 0.376408 0.567777 0.034678 0.397458 -0.053100 0.647124 0.276910 0.160827 0.631142 1.000000 0.553786 0.074908 0.128337 0.090808 -0.339098 -0.331159 -0.329005 -0.328355 -0.336131 0.588812 0.588812 0.588812 0.624646 0.628434 0.628434 0.628434 0.624646 0.643983 0.643983 0.643983 0.616911 0.614825 0.614825 0.614825 0.616911 0.446975 0.431218 0.431218 0.431218 0.446975 0.397495 0.293680 0.293680 0.293680 0.397495
mentality_penalties 0.071897 -0.320547 -0.265101 0.288142 0.251420 0.201538 0.181447 0.316899 0.646602 -0.005210 -0.004697 0.662849 0.862874 0.752145 0.776331 0.262622 0.595183 -0.644618 0.663582 0.813355 0.571513 0.663619 0.795701 0.760631 0.746328 0.738011 0.550824 0.758144 0.459133 0.448614 0.494055 0.257361 0.386284 0.772706 0.070303 0.484088 0.021208 0.789812 0.344994 0.127833 0.786006 0.553786 1.000000 0.104479 0.156818 0.114689 -0.644510 -0.640010 -0.637325 -0.642691 -0.645470 0.819145 0.819145 0.819145 0.799166 0.812367 0.812367 0.812367 0.799166 0.799480 0.799480 0.799480 0.781754 0.746818 0.746818 0.746818 0.781754 0.585662 0.552169 0.552169 0.552169 0.585662 0.537874 0.440939 0.440939 0.440939 0.537874
defending_marking 0.095487 -0.044097 -0.051303 0.199397 0.129611 0.043116 0.050547 0.025367 0.042260 -0.103717 0.003763 0.373159 0.141720 0.471315 0.355289 0.950341 0.644740 -0.514756 0.405365 -0.029025 0.553592 0.450958 0.062621 0.262768 0.246658 0.262319 0.504891 0.357393 0.080902 0.105869 0.038587 0.139241 0.079182 0.266954 0.189044 0.496106 0.286243 0.159072 0.680253 0.876480 0.134477 0.074908 0.104479 1.000000 0.946202 0.947263 -0.512059 -0.511903 -0.508248 -0.509167 -0.513108 0.343647 0.343647 0.343647 0.349419 0.338706 0.338706 0.338706 0.349419 0.369532 0.369532 0.369532 0.401499 0.519940 0.519940 0.519940 0.401499 0.749758 0.791030 0.791030 0.791030 0.749758 0.797458 0.868021 0.868021 0.868021 0.797458
defending_standing_tackle 0.085014 -0.052524 -0.063893 0.224607 0.162444 0.075388 0.073716 0.050568 0.097889 -0.102330 0.001320 0.414313 0.201477 0.526870 0.408721 0.962533 0.685592 -0.556996 0.448174 0.026011 0.582708 0.505908 0.118518 0.315544 0.301985 0.318619 0.550589 0.410996 0.106630 0.131338 0.067829 0.160398 0.097653 0.320677 0.185032 0.529711 0.295564 0.216314 0.709337 0.890864 0.192662 0.128337 0.156818 0.946202 1.000000 0.959425 -0.553528 -0.552902 -0.549392 -0.550899 -0.555346 0.399781 0.399781 0.399781 0.405141 0.396203 0.396203 0.396203 0.405141 0.427256 0.427256 0.427256 0.456293 0.576335 0.576335 0.576335 0.456293 0.788744 0.830142 0.830142 0.830142 0.788744 0.833216 0.896043 0.896043 0.896043 0.833216
defending_sliding_tackle 0.068744 -0.071170 -0.081469 0.191321 0.139049 0.056511 0.061860 0.030081 0.061889 -0.090515 0.009542 0.395538 0.154064 0.488785 0.375334 0.946758 0.643789 -0.526258 0.427599 -0.020106 0.543606 0.462825 0.076556 0.284408 0.268331 0.280936 0.517332 0.371727 0.110050 0.132122 0.067121 0.133325 0.111921 0.276584 0.192103 0.503195 0.255583 0.171608 0.678957 0.873848 0.153813 0.090808 0.114689 0.947263 0.959425 1.000000 -0.524035 -0.522774 -0.519861 -0.520562 -0.525231 0.356257 0.356257 0.356257 0.368155 0.355418 0.355418 0.355418 0.368155 0.386849 0.386849 0.386849 0.419834 0.534961 0.534961 0.534961 0.419834 0.764351 0.798299 0.798299 0.798299 0.764351 0.811201 0.870404 0.870404 0.870404 0.811201
goalkeeping_diving 0.110753 0.345080 0.337671 -0.013305 -0.054610 -0.011160 0.008567 -0.229314 -0.603506 -0.000528 -0.010811 -0.873950 -0.758078 -0.842429 -0.860783 -0.668346 -0.886312 0.959659 -0.706749 -0.627670 -0.787212 -0.694399 -0.629848 -0.772031 -0.661427 -0.628741 -0.593282 -0.784465 -0.493847 -0.495886 -0.442482 -0.000527 -0.401418 -0.697839 -0.101529 -0.642126 -0.084551 -0.654598 -0.558962 -0.453764 -0.706378 -0.339098 -0.644510 -0.512059 -0.553528 -0.524035 1.000000 0.965109 0.961118 0.965527 0.972006 -0.876228 -0.876228 -0.876228 -0.864253 -0.864122 -0.864122 -0.864122 -0.864253 -0.864870 -0.864870 -0.864870 -0.876887 -0.880697 -0.880697 -0.880697 -0.876887 -0.873948 -0.849979 -0.849979 -0.849979 -0.873948 -0.859059 -0.806065 -0.806065 -0.806065 -0.859059
goalkeeping_handling 0.118913 0.344673 0.339249 -0.008285 -0.052694 -0.010169 0.008862 -0.225646 -0.598528 -0.006068 -0.011434 -0.868274 -0.752263 -0.836956 -0.855039 -0.666287 -0.881632 0.951587 -0.702701 -0.622927 -0.784757 -0.689938 -0.625313 -0.766865 -0.656669 -0.623520 -0.588155 -0.780271 -0.491974 -0.495793 -0.440033 0.002699 -0.399526 -0.693702 -0.101283 -0.639228 -0.081549 -0.650759 -0.555333 -0.453750 -0.700799 -0.331159 -0.640010 -0.511903 -0.552902 -0.522774 0.965109 1.000000 0.960160 0.965378 0.966128 -0.870298 -0.870298 -0.870298 -0.858322 -0.858170 -0.858170 -0.858170 -0.858322 -0.858960 -0.858960 -0.858960 -0.871058 -0.875173 -0.875173 -0.875173 -0.871058 -0.869498 -0.845903 -0.845903 -0.845903 -0.869498 -0.854817 -0.802532 -0.802532 -0.802532 -0.854817
goalkeeping_kicking 0.114074 0.340714 0.334518 -0.011916 -0.054882 -0.012252 0.007609 -0.227129 -0.598083 -0.005026 -0.012459 -0.865116 -0.749882 -0.834337 -0.852224 -0.662787 -0.879163 0.950879 -0.699935 -0.619716 -0.779547 -0.686612 -0.621765 -0.763112 -0.654681 -0.620866 -0.585491 -0.776465 -0.488022 -0.490757 -0.439042 -0.000727 -0.395503 -0.690823 -0.099919 -0.636662 -0.084190 -0.646422 -0.553184 -0.448732 -0.698006 -0.329005 -0.637325 -0.508248 -0.549392 -0.519861 0.961118 0.960160 1.000000 0.959859 0.962098 -0.867379 -0.867379 -0.867379 -0.855536 -0.855347 -0.855347 -0.855347 -0.855536 -0.856093 -0.856093 -0.856093 -0.868190 -0.871984 -0.871984 -0.871984 -0.868190 -0.865872 -0.842329 -0.842329 -0.842329 -0.865872 -0.851173 -0.799097 -0.799097 -0.799097 -0.851173
goalkeeping_positioning 0.126850 0.346370 0.340144 -0.004463 -0.053324 -0.011187 0.010341 -0.227467 -0.599330 -0.006368 -0.013338 -0.868614 -0.753777 -0.837036 -0.855846 -0.662892 -0.879946 0.950258 -0.703763 -0.626061 -0.782220 -0.689461 -0.627386 -0.769358 -0.660964 -0.627084 -0.588310 -0.779936 -0.493843 -0.497843 -0.442835 0.005807 -0.401498 -0.694882 -0.099698 -0.638767 -0.078650 -0.651769 -0.551825 -0.449937 -0.703979 -0.328355 -0.642691 -0.509167 -0.550899 -0.520562 0.965527 0.965378 0.959859 1.000000 0.966918 -0.870844 -0.870844 -0.870844 -0.859059 -0.858909 -0.858909 -0.858909 -0.859059 -0.859540 -0.859540 -0.859540 -0.871543 -0.874876 -0.874876 -0.874876 -0.871543 -0.867852 -0.843831 -0.843831 -0.843831 -0.867852 -0.852858 -0.799867 -0.799867 -0.799867 -0.852858
goalkeeping_reflexes 0.109512 0.343593 0.334678 -0.013170 -0.053684 -0.011180 0.007519 -0.231161 -0.602399 -0.001365 -0.010358 -0.873302 -0.757140 -0.841635 -0.859972 -0.669583 -0.887810 0.958423 -0.705759 -0.626086 -0.787717 -0.693534 -0.628249 -0.771108 -0.660563 -0.626592 -0.591246 -0.784129 -0.493340 -0.496280 -0.441984 -0.001361 -0.399564 -0.696986 -0.102472 -0.643495 -0.087483 -0.653411 -0.560739 -0.455025 -0.705836 -0.336131 -0.645470 -0.513108 -0.555346 -0.525231 0.972006 0.966128 0.962098 0.966918 1.000000 -0.875834 -0.875834 -0.875834 -0.863498 -0.863480 -0.863480 -0.863480 -0.863498 -0.864134 -0.864134 -0.864134 -0.876241 -0.880201 -0.880201 -0.880201 -0.876241 -0.874259 -0.850509 -0.850509 -0.850509 -0.874259 -0.859623 -0.807174 -0.807174 -0.807174 -0.859623
ls 0.022931 -0.371797 -0.325837 0.321237 0.297491 0.206180 0.148768 0.338631 0.736327 -0.031245 -0.000028 0.895389 0.958163 0.940656 0.969502 0.526746 0.848474 -0.882866 0.822018 0.859337 0.775686 0.829176 0.843690 0.919107 0.829595 0.780259 0.704655 0.926808 0.604166 0.603702 0.594003 0.284001 0.459218 0.876177 0.139095 0.688676 0.098058 0.867660 0.554021 0.346869 0.903281 0.588812 0.819145 0.343647 0.399781 0.356257 -0.876228 -0.870298 -0.867379 -0.870844 -0.875834 1.000000 1.000000 1.000000 0.985759 0.993289 0.993289 0.993289 0.985759 0.985274 0.985274 0.985274 0.981555 0.959796 0.959796 0.959796 0.981555 0.850656 0.814169 0.814169 0.814169 0.850656 0.809747 0.713309 0.713309 0.713309 0.809747
st 0.022931 -0.371797 -0.325837 0.321237 0.297491 0.206180 0.148768 0.338631 0.736327 -0.031245 -0.000028 0.895389 0.958163 0.940656 0.969502 0.526746 0.848474 -0.882866 0.822018 0.859337 0.775686 0.829176 0.843690 0.919107 0.829595 0.780259 0.704655 0.926808 0.604166 0.603702 0.594003 0.284001 0.459218 0.876177 0.139095 0.688676 0.098058 0.867660 0.554021 0.346869 0.903281 0.588812 0.819145 0.343647 0.399781 0.356257 -0.876228 -0.870298 -0.867379 -0.870844 -0.875834 1.000000 1.000000 1.000000 0.985759 0.993289 0.993289 0.993289 0.985759 0.985274 0.985274 0.985274 0.981555 0.959796 0.959796 0.959796 0.981555 0.850656 0.814169 0.814169 0.814169 0.850656 0.809747 0.713309 0.713309 0.713309 0.809747
rs 0.022931 -0.371797 -0.325837 0.321237 0.297491 0.206180 0.148768 0.338631 0.736327 -0.031245 -0.000028 0.895389 0.958163 0.940656 0.969502 0.526746 0.848474 -0.882866 0.822018 0.859337 0.775686 0.829176 0.843690 0.919107 0.829595 0.780259 0.704655 0.926808 0.604166 0.603702 0.594003 0.284001 0.459218 0.876177 0.139095 0.688676 0.098058 0.867660 0.554021 0.346869 0.903281 0.588812 0.819145 0.343647 0.399781 0.356257 -0.876228 -0.870298 -0.867379 -0.870844 -0.875834 1.000000 1.000000 1.000000 0.985759 0.993289 0.993289 0.993289 0.985759 0.985274 0.985274 0.985274 0.981555 0.959796 0.959796 0.959796 0.981555 0.850656 0.814169 0.814169 0.814169 0.850656 0.809747 0.713309 0.713309 0.713309 0.809747
lw -0.003591 -0.455301 -0.407746 0.314071 0.306260 0.207746 0.139487 0.344032 0.757659 -0.026383 0.004863 0.916727 0.938259 0.965312 0.992113 0.523097 0.808134 -0.870797 0.875131 0.838406 0.702986 0.853626 0.829032 0.950743 0.857413 0.800120 0.741146 0.943087 0.661794 0.647820 0.661655 0.281018 0.537243 0.844244 0.118867 0.695161 0.004797 0.864848 0.526201 0.354886 0.904368 0.624646 0.799166 0.349419 0.405141 0.368155 -0.864253 -0.858322 -0.855536 -0.859059 -0.863498 0.985759 0.985759 0.985759 1.000000 0.997293 0.997293 0.997293 1.000000 0.996731 0.996731 0.996731 0.997319 0.970024 0.970024 0.970024 0.997319 0.863179 0.816256 0.816256 0.816256 0.863179 0.817004 0.699402 0.699402 0.699402 0.817004
lf 0.009109 -0.426990 -0.379890 0.324193 0.310361 0.213569 0.148277 0.346500 0.753949 -0.028186 0.003806 0.902720 0.951668 0.960350 0.986707 0.516990 0.816910 -0.870610 0.851572 0.852345 0.723562 0.852871 0.840686 0.943775 0.852536 0.799696 0.735400 0.943181 0.635606 0.624833 0.636283 0.292871 0.510825 0.862492 0.120011 0.689726 0.032616 0.874767 0.533836 0.347690 0.911966 0.628434 0.812367 0.338706 0.396203 0.355418 -0.864122 -0.858170 -0.855347 -0.858909 -0.863480 0.993289 0.993289 0.993289 0.997293 1.000000 1.000000 1.000000 0.997293 0.997128 0.997128 0.997128 0.993663 0.970026 0.970026 0.970026 0.993663 0.854643 0.813408 0.813408 0.813408 0.854643 0.809238 0.698344 0.698344 0.698344 0.809238
cf 0.009109 -0.426990 -0.379890 0.324193 0.310361 0.213569 0.148277 0.346500 0.753949 -0.028186 0.003806 0.902720 0.951668 0.960350 0.986707 0.516990 0.816910 -0.870610 0.851572 0.852345 0.723562 0.852871 0.840686 0.943775 0.852536 0.799696 0.735400 0.943181 0.635606 0.624833 0.636283 0.292871 0.510825 0.862492 0.120011 0.689726 0.032616 0.874767 0.533836 0.347690 0.911966 0.628434 0.812367 0.338706 0.396203 0.355418 -0.864122 -0.858170 -0.855347 -0.858909 -0.863480 0.993289 0.993289 0.993289 0.997293 1.000000 1.000000 1.000000 0.997293 0.997128 0.997128 0.997128 0.993663 0.970026 0.970026 0.970026 0.993663 0.854643 0.813408 0.813408 0.813408 0.854643 0.809238 0.698344 0.698344 0.698344 0.809238
rf 0.009109 -0.426990 -0.379890 0.324193 0.310361 0.213569 0.148277 0.346500 0.753949 -0.028186 0.003806 0.902720 0.951668 0.960350 0.986707 0.516990 0.816910 -0.870610 0.851572 0.852345 0.723562 0.852871 0.840686 0.943775 0.852536 0.799696 0.735400 0.943181 0.635606 0.624833 0.636283 0.292871 0.510825 0.862492 0.120011 0.689726 0.032616 0.874767 0.533836 0.347690 0.911966 0.628434 0.812367 0.338706 0.396203 0.355418 -0.864122 -0.858170 -0.855347 -0.858909 -0.863480 0.993289 0.993289 0.993289 0.997293 1.000000 1.000000 1.000000 0.997293 0.997128 0.997128 0.997128 0.993663 0.970026 0.970026 0.970026 0.993663 0.854643 0.813408 0.813408 0.813408 0.854643 0.809238 0.698344 0.698344 0.698344 0.809238
rw -0.003591 -0.455301 -0.407746 0.314071 0.306260 0.207746 0.139487 0.344032 0.757659 -0.026383 0.004863 0.916727 0.938259 0.965312 0.992113 0.523097 0.808134 -0.870797 0.875131 0.838406 0.702986 0.853626 0.829032 0.950743 0.857413 0.800120 0.741146 0.943087 0.661794 0.647820 0.661655 0.281018 0.537243 0.844244 0.118867 0.695161 0.004797 0.864848 0.526201 0.354886 0.904368 0.624646 0.799166 0.349419 0.405141 0.368155 -0.864253 -0.858322 -0.855536 -0.859059 -0.863498 0.985759 0.985759 0.985759 1.000000 0.997293 0.997293 0.997293 1.000000 0.996731 0.996731 0.996731 0.997319 0.970024 0.970024 0.970024 0.997319 0.863179 0.816256 0.816256 0.816256 0.863179 0.817004 0.699402 0.699402 0.699402 0.817004
lam 0.013235 -0.438802 -0.392627 0.328978 0.313618 0.214946 0.149761 0.345704 0.749065 -0.033223 0.003808 0.897215 0.936173 0.975713 0.988354 0.543815 0.817815 -0.871464 0.862944 0.830024 0.714178 0.875312 0.826355 0.942851 0.856633 0.807038 0.768470 0.947908 0.626725 0.612193 0.640666 0.295602 0.521549 0.850102 0.115986 0.695555 0.023525 0.868562 0.545512 0.380442 0.898125 0.643983 0.799480 0.369532 0.427256 0.386849 -0.864870 -0.858960 -0.856093 -0.859540 -0.864134 0.985274 0.985274 0.985274 0.996731 0.997128 0.997128 0.997128 0.996731 1.000000 1.000000 1.000000 0.996390 0.981111 0.981111 0.981111 0.996390 0.871561 0.834151 0.834151 0.834151 0.871561 0.827104 0.716930 0.716930 0.716930 0.827104
cam 0.013235 -0.438802 -0.392627 0.328978 0.313618 0.214946 0.149761 0.345704 0.749065 -0.033223 0.003808 0.897215 0.936173 0.975713 0.988354 0.543815 0.817815 -0.871464 0.862944 0.830024 0.714178 0.875312 0.826355 0.942851 0.856633 0.807038 0.768470 0.947908 0.626725 0.612193 0.640666 0.295602 0.521549 0.850102 0.115986 0.695555 0.023525 0.868562 0.545512 0.380442 0.898125 0.643983 0.799480 0.369532 0.427256 0.386849 -0.864870 -0.858960 -0.856093 -0.859540 -0.864134 0.985274 0.985274 0.985274 0.996731 0.997128 0.997128 0.997128 0.996731 1.000000 1.000000 1.000000 0.996390 0.981111 0.981111 0.981111 0.996390 0.871561 0.834151 0.834151 0.834151 0.871561 0.827104 0.716930 0.716930 0.716930 0.827104
ram 0.013235 -0.438802 -0.392627 0.328978 0.313618 0.214946 0.149761 0.345704 0.749065 -0.033223 0.003808 0.897215 0.936173 0.975713 0.988354 0.543815 0.817815 -0.871464 0.862944 0.830024 0.714178 0.875312 0.826355 0.942851 0.856633 0.807038 0.768470 0.947908 0.626725 0.612193 0.640666 0.295602 0.521549 0.850102 0.115986 0.695555 0.023525 0.868562 0.545512 0.380442 0.898125 0.643983 0.799480 0.369532 0.427256 0.386849 -0.864870 -0.858960 -0.856093 -0.859540 -0.864134 0.985274 0.985274 0.985274 0.996731 0.997128 0.997128 0.997128 0.996731 1.000000 1.000000 1.000000 0.996390 0.981111 0.981111 0.981111 0.996390 0.871561 0.834151 0.834151 0.834151 0.871561 0.827104 0.716930 0.716930 0.716930 0.827104
lm 0.001210 -0.445813 -0.400793 0.319067 0.306790 0.205865 0.138593 0.335959 0.741494 -0.034475 0.005199 0.918541 0.918355 0.975841 0.990349 0.571736 0.832759 -0.883713 0.881982 0.807571 0.719612 0.867623 0.807050 0.943085 0.850916 0.795987 0.765193 0.943578 0.649924 0.638277 0.643769 0.282749 0.528261 0.836326 0.126938 0.720161 0.024305 0.846400 0.558113 0.404170 0.887381 0.616911 0.781754 0.401499 0.456293 0.419834 -0.876887 -0.871058 -0.868190 -0.871543 -0.876241 0.981555 0.981555 0.981555 0.997319 0.993663 0.993663 0.993663 0.997319 0.996390 0.996390 0.996390 1.000000 0.982114 0.982114 0.982114 1.000000 0.892433 0.849919 0.849919 0.849919 0.892433 0.850105 0.739007 0.739007 0.739007 0.850105
lcm 0.050825 -0.391435 -0.350573 0.355663 0.317066 0.214490 0.159101 0.321143 0.684234 -0.058140 0.000444 0.868593 0.876037 0.987252 0.962021 0.683010 0.874412 -0.887769 0.859988 0.739998 0.758542 0.900025 0.760627 0.898746 0.827431 0.791672 0.823421 0.933271 0.554812 0.547184 0.570597 0.313722 0.473680 0.828000 0.140109 0.737206 0.093435 0.822542 0.639390 0.530829 0.835864 0.614825 0.746818 0.519940 0.576335 0.534961 -0.880697 -0.875173 -0.871984 -0.874876 -0.880201 0.959796 0.959796 0.959796 0.970024 0.970026 0.970026 0.970026 0.970024 0.981111 0.981111 0.981111 0.982114 1.000000 1.000000 1.000000 0.982114 0.939707 0.920290 0.920290 0.920290 0.939707 0.908444 0.825073 0.825073 0.825073 0.908444
cm 0.050825 -0.391435 -0.350573 0.355663 0.317066 0.214490 0.159101 0.321143 0.684234 -0.058140 0.000444 0.868593 0.876037 0.987252 0.962021 0.683010 0.874412 -0.887769 0.859988 0.739998 0.758542 0.900025 0.760627 0.898746 0.827431 0.791672 0.823421 0.933271 0.554812 0.547184 0.570597 0.313722 0.473680 0.828000 0.140109 0.737206 0.093435 0.822542 0.639390 0.530829 0.835864 0.614825 0.746818 0.519940 0.576335 0.534961 -0.880697 -0.875173 -0.871984 -0.874876 -0.880201 0.959796 0.959796 0.959796 0.970024 0.970026 0.970026 0.970026 0.970024 0.981111 0.981111 0.981111 0.982114 1.000000 1.000000 1.000000 0.982114 0.939707 0.920290 0.920290 0.920290 0.939707 0.908444 0.825073 0.825073 0.825073 0.908444
rcm 0.050825 -0.391435 -0.350573 0.355663 0.317066 0.214490 0.159101 0.321143 0.684234 -0.058140 0.000444 0.868593 0.876037 0.987252 0.962021 0.683010 0.874412 -0.887769 0.859988 0.739998 0.758542 0.900025 0.760627 0.898746 0.827431 0.791672 0.823421 0.933271 0.554812 0.547184 0.570597 0.313722 0.473680 0.828000 0.140109 0.737206 0.093435 0.822542 0.639390 0.530829 0.835864 0.614825 0.746818 0.519940 0.576335 0.534961 -0.880697 -0.875173 -0.871984 -0.874876 -0.880201 0.959796 0.959796 0.959796 0.970024 0.970026 0.970026 0.970026 0.970024 0.981111 0.981111 0.981111 0.982114 1.000000 1.000000 1.000000 0.982114 0.939707 0.920290 0.920290 0.920290 0.939707 0.908444 0.825073 0.825073 0.825073 0.908444
rm 0.001210 -0.445813 -0.400793 0.319067 0.306790 0.205865 0.138593 0.335959 0.741494 -0.034475 0.005199 0.918541 0.918355 0.975841 0.990349 0.571736 0.832759 -0.883713 0.881982 0.807571 0.719612 0.867623 0.807050 0.943085 0.850916 0.795987 0.765193 0.943578 0.649924 0.638277 0.643769 0.282749 0.528261 0.836326 0.126938 0.720161 0.024305 0.846400 0.558113 0.404170 0.887381 0.616911 0.781754 0.401499 0.456293 0.419834 -0.876887 -0.871058 -0.868190 -0.871543 -0.876241 0.981555 0.981555 0.981555 0.997319 0.993663 0.993663 0.993663 0.997319 0.996390 0.996390 0.996390 1.000000 0.982114 0.982114 0.982114 1.000000 0.892433 0.849919 0.849919 0.849919 0.892433 0.850105 0.739007 0.739007 0.739007 0.850105
lwb 0.046002 -0.325603 -0.298411 0.314825 0.270101 0.164308 0.122380 0.239683 0.531699 -0.078242 0.001424 0.832935 0.698758 0.908033 0.860507 0.875960 0.915032 -0.881761 0.808197 0.528117 0.781133 0.817747 0.577657 0.772177 0.702371 0.672190 0.769808 0.824830 0.493498 0.501434 0.463525 0.260861 0.400874 0.705215 0.196337 0.764014 0.168889 0.652589 0.734497 0.728475 0.668715 0.446975 0.585662 0.749758 0.788744 0.764351 -0.873948 -0.869498 -0.865872 -0.867852 -0.874259 0.850656 0.850656 0.850656 0.863179 0.854643 0.854643 0.854643 0.863179 0.871561 0.871561 0.871561 0.892433 0.939707 0.939707 0.939707 0.892433 1.000000 0.989661 0.989661 0.989661 1.000000 0.995527 0.954171 0.954171 0.954171 0.995527
ldm 0.084582 -0.258504 -0.233983 0.331386 0.267885 0.164751 0.134257 0.223933 0.478510 -0.092148 -0.003559 0.764888 0.656536 0.883237 0.812096 0.911355 0.920885 -0.857812 0.751275 0.477998 0.794573 0.813068 0.535637 0.715617 0.659306 0.645073 0.781576 0.792925 0.398022 0.408964 0.385108 0.273106 0.332768 0.684018 0.196919 0.736461 0.233944 0.617293 0.775185 0.776615 0.616742 0.431218 0.552169 0.791030 0.830142 0.798299 -0.849979 -0.845903 -0.842329 -0.843831 -0.850509 0.814169 0.814169 0.814169 0.816256 0.813408 0.813408 0.813408 0.816256 0.834151 0.834151 0.834151 0.849919 0.920290 0.920290 0.920290 0.849919 0.989661 1.000000 1.000000 1.000000 0.989661 0.992773 0.976520 0.976520 0.976520 0.992773
cdm 0.084582 -0.258504 -0.233983 0.331386 0.267885 0.164751 0.134257 0.223933 0.478510 -0.092148 -0.003559 0.764888 0.656536 0.883237 0.812096 0.911355 0.920885 -0.857812 0.751275 0.477998 0.794573 0.813068 0.535637 0.715617 0.659306 0.645073 0.781576 0.792925 0.398022 0.408964 0.385108 0.273106 0.332768 0.684018 0.196919 0.736461 0.233944 0.617293 0.775185 0.776615 0.616742 0.431218 0.552169 0.791030 0.830142 0.798299 -0.849979 -0.845903 -0.842329 -0.843831 -0.850509 0.814169 0.814169 0.814169 0.816256 0.813408 0.813408 0.813408 0.816256 0.834151 0.834151 0.834151 0.849919 0.920290 0.920290 0.920290 0.849919 0.989661 1.000000 1.000000 1.000000 0.989661 0.992773 0.976520 0.976520 0.976520 0.992773
rdm 0.084582 -0.258504 -0.233983 0.331386 0.267885 0.164751 0.134257 0.223933 0.478510 -0.092148 -0.003559 0.764888 0.656536 0.883237 0.812096 0.911355 0.920885 -0.857812 0.751275 0.477998 0.794573 0.813068 0.535637 0.715617 0.659306 0.645073 0.781576 0.792925 0.398022 0.408964 0.385108 0.273106 0.332768 0.684018 0.196919 0.736461 0.233944 0.617293 0.775185 0.776615 0.616742 0.431218 0.552169 0.791030 0.830142 0.798299 -0.849979 -0.845903 -0.842329 -0.843831 -0.850509 0.814169 0.814169 0.814169 0.816256 0.813408 0.813408 0.813408 0.816256 0.834151 0.834151 0.834151 0.849919 0.920290 0.920290 0.920290 0.849919 0.989661 1.000000 1.000000 1.000000 0.989661 0.992773 0.976520 0.976520 0.976520 0.992773
rwb 0.046002 -0.325603 -0.298411 0.314825 0.270101 0.164308 0.122380 0.239683 0.531699 -0.078242 0.001424 0.832935 0.698758 0.908033 0.860507 0.875960 0.915032 -0.881761 0.808197 0.528117 0.781133 0.817747 0.577657 0.772177 0.702371 0.672190 0.769808 0.824830 0.493498 0.501434 0.463525 0.260861 0.400874 0.705215 0.196337 0.764014 0.168889 0.652589 0.734497 0.728475 0.668715 0.446975 0.585662 0.749758 0.788744 0.764351 -0.873948 -0.869498 -0.865872 -0.867852 -0.874259 0.850656 0.850656 0.850656 0.863179 0.854643 0.854643 0.854643 0.863179 0.871561 0.871561 0.871561 0.892433 0.939707 0.939707 0.939707 0.892433 1.000000 0.989661 0.989661 0.989661 1.000000 0.995527 0.954171 0.954171 0.954171 0.995527
lb 0.049920 -0.285420 -0.264230 0.302586 0.255058 0.150920 0.115759 0.214493 0.477996 -0.083102 0.001553 0.800295 0.643723 0.869601 0.814175 0.914383 0.915544 -0.866853 0.764308 0.466539 0.789995 0.783080 0.521969 0.718024 0.651881 0.627274 0.744346 0.780885 0.450753 0.463781 0.411821 0.245538 0.358966 0.665902 0.209495 0.748729 0.201371 0.598755 0.752965 0.767959 0.612659 0.397495 0.537874 0.797458 0.833216 0.811201 -0.859059 -0.854817 -0.851173 -0.852858 -0.859623 0.809747 0.809747 0.809747 0.817004 0.809238 0.809238 0.809238 0.817004 0.827104 0.827104 0.827104 0.850105 0.908444 0.908444 0.908444 0.850105 0.995527 0.992773 0.992773 0.992773 0.995527 1.000000 0.975842 0.975842 0.975842 1.000000
lcb 0.091531 -0.152169 -0.137809 0.287742 0.217449 0.123135 0.111357 0.161987 0.352840 -0.098735 -0.004776 0.684496 0.527674 0.770362 0.695236 0.966683 0.914284 -0.813820 0.640622 0.341281 0.811381 0.700933 0.407616 0.586209 0.530464 0.524233 0.677501 0.675669 0.298822 0.325201 0.259553 0.222267 0.222051 0.585250 0.236918 0.685520 0.323689 0.484314 0.800709 0.829185 0.486627 0.293680 0.440939 0.868021 0.896043 0.870404 -0.806065 -0.802532 -0.799097 -0.799867 -0.807174 0.713309 0.713309 0.713309 0.699402 0.698344 0.698344 0.698344 0.699402 0.716930 0.716930 0.716930 0.739007 0.825073 0.825073 0.825073 0.739007 0.954171 0.976520 0.976520 0.976520 0.954171 0.975842 1.000000 1.000000 1.000000 0.975842
cb 0.091531 -0.152169 -0.137809 0.287742 0.217449 0.123135 0.111357 0.161987 0.352840 -0.098735 -0.004776 0.684496 0.527674 0.770362 0.695236 0.966683 0.914284 -0.813820 0.640622 0.341281 0.811381 0.700933 0.407616 0.586209 0.530464 0.524233 0.677501 0.675669 0.298822 0.325201 0.259553 0.222267 0.222051 0.585250 0.236918 0.685520 0.323689 0.484314 0.800709 0.829185 0.486627 0.293680 0.440939 0.868021 0.896043 0.870404 -0.806065 -0.802532 -0.799097 -0.799867 -0.807174 0.713309 0.713309 0.713309 0.699402 0.698344 0.698344 0.698344 0.699402 0.716930 0.716930 0.716930 0.739007 0.825073 0.825073 0.825073 0.739007 0.954171 0.976520 0.976520 0.976520 0.954171 0.975842 1.000000 1.000000 1.000000 0.975842
rcb 0.091531 -0.152169 -0.137809 0.287742 0.217449 0.123135 0.111357 0.161987 0.352840 -0.098735 -0.004776 0.684496 0.527674 0.770362 0.695236 0.966683 0.914284 -0.813820 0.640622 0.341281 0.811381 0.700933 0.407616 0.586209 0.530464 0.524233 0.677501 0.675669 0.298822 0.325201 0.259553 0.222267 0.222051 0.585250 0.236918 0.685520 0.323689 0.484314 0.800709 0.829185 0.486627 0.293680 0.440939 0.868021 0.896043 0.870404 -0.806065 -0.802532 -0.799097 -0.799867 -0.807174 0.713309 0.713309 0.713309 0.699402 0.698344 0.698344 0.698344 0.699402 0.716930 0.716930 0.716930 0.739007 0.825073 0.825073 0.825073 0.739007 0.954171 0.976520 0.976520 0.976520 0.954171 0.975842 1.000000 1.000000 1.000000 0.975842
rb 0.049920 -0.285420 -0.264230 0.302586 0.255058 0.150920 0.115759 0.214493 0.477996 -0.083102 0.001553 0.800295 0.643723 0.869601 0.814175 0.914383 0.915544 -0.866853 0.764308 0.466539 0.789995 0.783080 0.521969 0.718024 0.651881 0.627274 0.744346 0.780885 0.450753 0.463781 0.411821 0.245538 0.358966 0.665902 0.209495 0.748729 0.201371 0.598755 0.752965 0.767959 0.612659 0.397495 0.537874 0.797458 0.833216 0.811201 -0.859059 -0.854817 -0.851173 -0.852858 -0.859623 0.809747 0.809747 0.809747 0.817004 0.809238 0.809238 0.809238 0.817004 0.827104 0.827104 0.827104 0.850105 0.908444 0.908444 0.908444 0.850105 0.995527 0.992773 0.992773 0.992773 0.995527 1.000000 0.975842 0.975842 0.975842 1.000000

From the correlation we can see that maximum correlation the value has with international reputation and overall rating of the player. Which is logical.

The Model

Linear Regression

Linear Regression model is used when we need to predict a continuous variable using a linear function of type $y=ax+b$. $a$ and $b$ are unknown parameters. Given pairs $(x,y)$ we create a model under the assumption that $y$ depends linearly on $x$. [4]

Below is the representation of data and its linear regression model taken from Wikipedia.

LinRegr

The red line is the approximation of the data and for one independednt variable $x$, the dependent variable $y$ will look like: $y=\theta_0+\theta_1*x_1$ (or $b+ax$). If we have more independent variables (called features) $x_1,x_2,...,x_n$ then $y=\theta_0+\theta_1*x_1+\theta_2*x_2+...+\theta_n*x_n$.

Here we will create a model with 78 features, which after converting categorical variables to numerical will become 114.

In [82]:
players_16.head().transpose()
Out[82]:
sofifa_id 158023 20801 9014 167495 176580
short_name L. Messi Cristiano Ronaldo A. Robben M. Neuer L. Suárez
age 28 30 31 29 28
dob 1987-06-24 1985-02-05 1984-01-23 1986-03-27 1987-01-24
height_cm 170 185 180 193 182
weight_kg 72 80 80 92 85
nationality Argentina Portugal Netherlands Germany Uruguay
club FC Barcelona Real Madrid FC Bayern München FC Bayern München FC Barcelona
overall 94 93 90 90 90
potential 95 93 90 90 90
value_eur 111000000 85500000 56000000 58000000 69000000
player_positions RW, CF LW, LM RM, LM, RW GK ST
preferred_foot Left Right Left Right Right
international_reputation 5 5 5 5 5
weak_foot 4 4 2 4 4
skill_moves 4 5 4 1 4
work_rate Medium/Low High/Low High/Low Medium/Medium High/Medium
body_type Messi C. Ronaldo Normal Normal Normal
real_face Yes Yes Yes Yes Yes
team_position RW LM SUB GK ST
team_jersey_number 10 7 10 1 9
joined 2004-07-01 2009-07-01 2009-08-28 2011-07-01 2014-07-11
contract_valid_until 2018 2018 2017 2019 2019
pace 92 92 92 0 83
shooting 88 93 86 0 88
passing 86 80 82 0 79
dribbling 95 91 92 0 87
defending 24 33 32 0 42
physic 62 78 64 0 79
gk_speed 0 0 0 60 0
player_traits Finesse Shot, Speed Dribbler (CPU AI Only), On... Power Free-Kick, Flair, Long Shot Taker (CPU A... Diver, Injury Prone, Avoids Using Weaker Foot,... GK Long Throw, 1-on-1 Rush Diver, Beat Offside Trap, Flair, Technical Dri...
attacking_crossing 76 81 80 5 77
attacking_finishing 92 95 85 1 89
attacking_heading_accuracy 71 86 50 25 79
attacking_short_passing 87 80 84 54 82
attacking_volleys 85 87 86 -3 89
skill_dribbling 96 93 93 7 86
skill_curve 89 88 87 3 86
skill_fk_accuracy 90 75 83 -3 84
skill_long_passing 82 72 72 53 64
skill_ball_control 96 90 88 31 93
movement_acceleration 94 91 91 58 88
movement_sprint_speed 90 92 91 61 77
movement_agility 90 87 89 43 86
movement_reactions 90 94 91 88 91
movement_balance 95 61 91 35 60
power_shot_power 80 94 86 8 92
power_jumping 63 94 61 78 69
power_stamina 75 85 74 44 86
power_strength 58 79 65 83 76
power_long_shots 88 93 90 7 88
mentality_aggression 48 61 47 29 78
mentality_interceptions 22 34 39 30 41
mentality_positioning 88 95 89 -1 94
mentality_vision 90 81 84 90 84
mentality_penalties 74 85 80 37 85
defending_marking 1 22 29 -5 30
defending_standing_tackle 25 31 26 -5 45
defending_sliding_tackle 22 23 26 -3 38
goalkeeping_diving 6 7 10 82 27
goalkeeping_handling 11 11 8 89 25
goalkeeping_kicking 15 15 11 91 31
goalkeeping_positioning 14 14 5 90 33
goalkeeping_reflexes 8 11 15 86 37
ls 90 94 87 0 90
st 90 94 87 0 90
rs 90 94 87 0 90
lw 94 93 92 0 90
lf 94 94 91 0 91
cf 94 94 91 0 91
rf 94 94 91 0 91
rw 94 93 92 0 90
lam 94 91 91 0 89
cam 94 91 91 0 89
ram 94 91 91 0 89
lm 93 91 90 0 88
lcm 85 83 83 0 82
cm 85 83 83 0 82
rcm 85 83 83 0 82
rm 93 91 90 0 88
lwb 65 67 68 0 70
ldm 60 63 63 0 68
cdm 60 63 63 0 68
rdm 60 63 63 0 68
rwb 65 67 68 0 70
lb 60 63 62 0 67
lcb 47 55 50 0 61
cb 47 55 50 0 61
rcb 47 55 50 0 61
rb 60 63 62 0 67

Before submitting the dataset to any ML algorithm, we need to do some more preprocessing. Column "dob" (date of birth) can be removed since column "age" is there and they both give same information. Names of players should be removed, as well as "players_traits", "joined", "body_type", "real_face" and "contract_valid_until". These are hardly supposed to influence the value of the player. "players_positions" can also be removed since the values in the columns with the respective positions depict players capabilities in each position. Naturally, they will have higher values for the positions the player takes.

In [83]:
players_16=players_16.drop(["short_name","dob","player_traits", "joined", "body_type", "real_face","contract_valid_until","nationality","player_positions","team_jersey_number","club"],axis=1)
In [84]:
players_16.head().transpose()
Out[84]:
sofifa_id 158023 20801 9014 167495 176580
age 28 30 31 29 28
height_cm 170 185 180 193 182
weight_kg 72 80 80 92 85
overall 94 93 90 90 90
potential 95 93 90 90 90
value_eur 111000000 85500000 56000000 58000000 69000000
preferred_foot Left Right Left Right Right
international_reputation 5 5 5 5 5
weak_foot 4 4 2 4 4
skill_moves 4 5 4 1 4
work_rate Medium/Low High/Low High/Low Medium/Medium High/Medium
team_position RW LM SUB GK ST
pace 92 92 92 0 83
shooting 88 93 86 0 88
passing 86 80 82 0 79
dribbling 95 91 92 0 87
defending 24 33 32 0 42
physic 62 78 64 0 79
gk_speed 0 0 0 60 0
attacking_crossing 76 81 80 5 77
attacking_finishing 92 95 85 1 89
attacking_heading_accuracy 71 86 50 25 79
attacking_short_passing 87 80 84 54 82
attacking_volleys 85 87 86 -3 89
skill_dribbling 96 93 93 7 86
skill_curve 89 88 87 3 86
skill_fk_accuracy 90 75 83 -3 84
skill_long_passing 82 72 72 53 64
skill_ball_control 96 90 88 31 93
movement_acceleration 94 91 91 58 88
movement_sprint_speed 90 92 91 61 77
movement_agility 90 87 89 43 86
movement_reactions 90 94 91 88 91
movement_balance 95 61 91 35 60
power_shot_power 80 94 86 8 92
power_jumping 63 94 61 78 69
power_stamina 75 85 74 44 86
power_strength 58 79 65 83 76
power_long_shots 88 93 90 7 88
mentality_aggression 48 61 47 29 78
mentality_interceptions 22 34 39 30 41
mentality_positioning 88 95 89 -1 94
mentality_vision 90 81 84 90 84
mentality_penalties 74 85 80 37 85
defending_marking 1 22 29 -5 30
defending_standing_tackle 25 31 26 -5 45
defending_sliding_tackle 22 23 26 -3 38
goalkeeping_diving 6 7 10 82 27
goalkeeping_handling 11 11 8 89 25
goalkeeping_kicking 15 15 11 91 31
goalkeeping_positioning 14 14 5 90 33
goalkeeping_reflexes 8 11 15 86 37
ls 90 94 87 0 90
st 90 94 87 0 90
rs 90 94 87 0 90
lw 94 93 92 0 90
lf 94 94 91 0 91
cf 94 94 91 0 91
rf 94 94 91 0 91
rw 94 93 92 0 90
lam 94 91 91 0 89
cam 94 91 91 0 89
ram 94 91 91 0 89
lm 93 91 90 0 88
lcm 85 83 83 0 82
cm 85 83 83 0 82
rcm 85 83 83 0 82
rm 93 91 90 0 88
lwb 65 67 68 0 70
ldm 60 63 63 0 68
cdm 60 63 63 0 68
rdm 60 63 63 0 68
rwb 65 67 68 0 70
lb 60 63 62 0 67
lcb 47 55 50 0 61
cb 47 55 50 0 61
rcb 47 55 50 0 61
rb 60 63 62 0 67

We will rearrange columns little bit by putting the target attribute at the beginning of the set so we can easily remove it later.

In [85]:
players_16.columns
Out[85]:
Index(['age', 'height_cm', 'weight_kg', 'overall', 'potential', 'value_eur',
       'preferred_foot', 'international_reputation', 'weak_foot',
       'skill_moves', 'work_rate', 'team_position', 'pace', 'shooting',
       'passing', 'dribbling', 'defending', 'physic', 'gk_speed',
       'attacking_crossing', 'attacking_finishing',
       'attacking_heading_accuracy', 'attacking_short_passing',
       'attacking_volleys', 'skill_dribbling', 'skill_curve',
       'skill_fk_accuracy', 'skill_long_passing', 'skill_ball_control',
       'movement_acceleration', 'movement_sprint_speed', 'movement_agility',
       'movement_reactions', 'movement_balance', 'power_shot_power',
       'power_jumping', 'power_stamina', 'power_strength', 'power_long_shots',
       'mentality_aggression', 'mentality_interceptions',
       'mentality_positioning', 'mentality_vision', 'mentality_penalties',
       'defending_marking', 'defending_standing_tackle',
       'defending_sliding_tackle', 'goalkeeping_diving',
       'goalkeeping_handling', 'goalkeeping_kicking',
       'goalkeeping_positioning', 'goalkeeping_reflexes', 'ls', 'st', 'rs',
       'lw', 'lf', 'cf', 'rf', 'rw', 'lam', 'cam', 'ram', 'lm', 'lcm', 'cm',
       'rcm', 'rm', 'lwb', 'ldm', 'cdm', 'rdm', 'rwb', 'lb', 'lcb', 'cb',
       'rcb', 'rb'],
      dtype='object')
In [86]:
players_16=players_16[['value_eur','age', 'height_cm', 'weight_kg', 'overall', 'potential',
       'preferred_foot', 'international_reputation', 'weak_foot',
       'skill_moves', 'work_rate', 'team_position', 'pace', 'shooting',
       'passing', 'dribbling', 'defending', 'physic', 'gk_speed',
       'attacking_crossing', 'attacking_finishing',
       'attacking_heading_accuracy', 'attacking_short_passing',
       'attacking_volleys', 'skill_dribbling', 'skill_curve',
       'skill_fk_accuracy', 'skill_long_passing', 'skill_ball_control',
       'movement_acceleration', 'movement_sprint_speed', 'movement_agility',
       'movement_reactions', 'movement_balance', 'power_shot_power',
       'power_jumping', 'power_stamina', 'power_strength', 'power_long_shots',
       'mentality_aggression', 'mentality_interceptions',
       'mentality_positioning', 'mentality_vision', 'mentality_penalties',
       'defending_marking', 'defending_standing_tackle',
       'defending_sliding_tackle', 'goalkeeping_diving',
       'goalkeeping_handling', 'goalkeeping_kicking',
       'goalkeeping_positioning', 'goalkeeping_reflexes', 'ls', 'st', 'rs',
       'lw', 'lf', 'cf', 'rf', 'rw', 'lam', 'cam', 'ram', 'lm', 'lcm', 'cm',
       'rcm', 'rm', 'lwb', 'ldm', 'cdm', 'rdm', 'rwb', 'lb', 'lcb', 'cb',
       'rcb', 'rb']]
In [87]:
players_16.head()
Out[87]:
value_eur age height_cm weight_kg overall potential preferred_foot international_reputation weak_foot skill_moves work_rate team_position pace shooting passing dribbling defending physic gk_speed attacking_crossing attacking_finishing attacking_heading_accuracy attacking_short_passing attacking_volleys skill_dribbling skill_curve skill_fk_accuracy skill_long_passing skill_ball_control movement_acceleration movement_sprint_speed movement_agility movement_reactions movement_balance power_shot_power power_jumping power_stamina power_strength power_long_shots mentality_aggression mentality_interceptions mentality_positioning mentality_vision mentality_penalties defending_marking defending_standing_tackle defending_sliding_tackle goalkeeping_diving goalkeeping_handling goalkeeping_kicking goalkeeping_positioning goalkeeping_reflexes ls st rs lw lf cf rf rw lam cam ram lm lcm cm rcm rm lwb ldm cdm rdm rwb lb lcb cb rcb rb
sofifa_id
158023 111000000 28 170 72 94 95 Left 5 4 4 Medium/Low RW 92.0 88.0 86.0 95.0 24.0 62.0 0.0 76 92 71 87 85 96 89 90 82 96 94 90 90 90 95 80 63 75 58 88 48 22 88 90 74 1 25 22 6 11 15 14 8 90 90 90 94 94 94 94 94 94 94 94 93 85 85 85 93 65 60 60 60 65 60 47 47 47 60
20801 85500000 30 185 80 93 93 Right 5 4 5 High/Low LM 92.0 93.0 80.0 91.0 33.0 78.0 0.0 81 95 86 80 87 93 88 75 72 90 91 92 87 94 61 94 94 85 79 93 61 34 95 81 85 22 31 23 7 11 15 14 11 94 94 94 93 94 94 94 93 91 91 91 91 83 83 83 91 67 63 63 63 67 63 55 55 55 63
9014 56000000 31 180 80 90 90 Left 5 2 4 High/Low SUB 92.0 86.0 82.0 92.0 32.0 64.0 0.0 80 85 50 84 86 93 87 83 72 88 91 91 89 91 91 86 61 74 65 90 47 39 89 84 80 29 26 26 10 8 11 5 15 87 87 87 92 91 91 91 92 91 91 91 90 83 83 83 90 68 63 63 63 68 62 50 50 50 62
167495 58000000 29 193 92 90 90 Right 5 4 1 Medium/Medium GK 0.0 0.0 0.0 0.0 0.0 0.0 60.0 5 1 25 54 -3 7 3 -3 53 31 58 61 43 88 35 8 78 44 83 7 29 30 -1 90 37 -5 -5 -3 82 89 91 90 86 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
176580 69000000 28 182 85 90 90 Right 5 4 4 High/Medium ST 83.0 88.0 79.0 87.0 42.0 79.0 0.0 77 89 79 82 89 86 86 84 64 93 88 77 86 91 60 92 69 86 76 88 78 41 94 84 85 30 45 38 27 25 31 33 37 90 90 90 90 91 91 91 90 89 89 89 88 82 82 82 88 70 68 68 68 70 67 61 61 61 67
In [88]:
players_16.shape
Out[88]:
(14881, 78)

We are going to apply Linear Regression in order to train a model to predict players' price. scikit-learn will be used. There are few steps before that.

  1. Converting categorical values to numerical
  2. Normalization
  3. Splitting the data

Converting categorical values to numerical

ML algorithms cannot work with categorical features, that's why they have to be converted to numerical. One way is by encoding (substituting) each value of a categorical feature by a binary number with n digits (actually n-1), where n is the number of unique values of the feature.

In [89]:
dummy_features_foot=pd.get_dummies(players_16["preferred_foot"])
dummy_features_foot.columns
Out[89]:
Index(['Left', 'Right'], dtype='object')
In [90]:
dummy_features_position=pd.get_dummies(players_16["team_position"])
dummy_features_position.columns
Out[90]:
Index(['CAM', 'CB', 'CDM', 'CF', 'CM', 'GK', 'LAM', 'LB', 'LCB', 'LCM', 'LDM',
       'LF', 'LM', 'LS', 'LW', 'LWB', 'RAM', 'RB', 'RCB', 'RCM', 'RDM', 'RES',
       'RF', 'RM', 'RS', 'RW', 'RWB', 'ST', 'SUB'],
      dtype='object')
In [91]:
dummy_features_rate=pd.get_dummies(players_16["work_rate"])
dummy_features_rate.columns
Out[91]:
Index(['High/High', 'High/Low', 'High/Medium', 'Low/High', 'Low/Low',
       'Low/Medium', 'Medium/High', 'Medium/Low', 'Medium/Medium'],
      dtype='object')
In [92]:
players_16=players_16.drop(["preferred_foot","team_position","work_rate"],axis=1)
In [93]:
players_16[['Left', 'Right']]=dummy_features_foot
players_16[['CAM', 'CB', 'CDM', 'CF', 'CM', 'GK', 'LAM', 'LB', 'LCB', 'LCM', 'LDM',
       'LF', 'LM', 'LS', 'LW', 'LWB', 'RAM', 'RB', 'RCB', 'RCM', 'RDM', 'RES',
       'RF', 'RM', 'RS', 'RW', 'RWB', 'ST', 'SUB']]=dummy_features_position
players_16[['High/High', 'High/Low', 'High/Medium', 'Low/High', 'Low/Low',
       'Low/Medium', 'Medium/High', 'Medium/Low', 'Medium/Medium']]=dummy_features_rate
In [94]:
players_16.head()
Out[94]:
value_eur age height_cm weight_kg overall potential international_reputation weak_foot skill_moves pace shooting passing dribbling defending physic gk_speed attacking_crossing attacking_finishing attacking_heading_accuracy attacking_short_passing attacking_volleys skill_dribbling skill_curve skill_fk_accuracy skill_long_passing skill_ball_control movement_acceleration movement_sprint_speed movement_agility movement_reactions movement_balance power_shot_power power_jumping power_stamina power_strength power_long_shots mentality_aggression mentality_interceptions mentality_positioning mentality_vision mentality_penalties defending_marking defending_standing_tackle defending_sliding_tackle goalkeeping_diving goalkeeping_handling goalkeeping_kicking goalkeeping_positioning goalkeeping_reflexes ls st rs lw lf cf rf rw lam cam ram lm lcm cm rcm rm lwb ldm cdm rdm rwb lb lcb cb rcb rb Left Right CAM CB CDM CF CM GK LAM LB LCB LCM LDM LF LM LS LW LWB RAM RB RCB RCM RDM RES RF RM RS RW RWB ST SUB High/High High/Low High/Medium Low/High Low/Low Low/Medium Medium/High Medium/Low Medium/Medium
sofifa_id
158023 111000000 28 170 72 94 95 5 4 4 92.0 88.0 86.0 95.0 24.0 62.0 0.0 76 92 71 87 85 96 89 90 82 96 94 90 90 90 95 80 63 75 58 88 48 22 88 90 74 1 25 22 6 11 15 14 8 90 90 90 94 94 94 94 94 94 94 94 93 85 85 85 93 65 60 60 60 65 60 47 47 47 60 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0
20801 85500000 30 185 80 93 93 5 4 5 92.0 93.0 80.0 91.0 33.0 78.0 0.0 81 95 86 80 87 93 88 75 72 90 91 92 87 94 61 94 94 85 79 93 61 34 95 81 85 22 31 23 7 11 15 14 11 94 94 94 93 94 94 94 93 91 91 91 91 83 83 83 91 67 63 63 63 67 63 55 55 55 63 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
9014 56000000 31 180 80 90 90 5 2 4 92.0 86.0 82.0 92.0 32.0 64.0 0.0 80 85 50 84 86 93 87 83 72 88 91 91 89 91 91 86 61 74 65 90 47 39 89 84 80 29 26 26 10 8 11 5 15 87 87 87 92 91 91 91 92 91 91 91 90 83 83 83 90 68 63 63 63 68 62 50 50 50 62 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0
167495 58000000 29 193 92 90 90 5 4 1 0.0 0.0 0.0 0.0 0.0 0.0 60.0 5 1 25 54 -3 7 3 -3 53 31 58 61 43 88 35 8 78 44 83 7 29 30 -1 90 37 -5 -5 -3 82 89 91 90 86 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
176580 69000000 28 182 85 90 90 5 4 4 83.0 88.0 79.0 87.0 42.0 79.0 0.0 77 89 79 82 89 86 86 84 64 93 88 77 86 91 60 92 69 86 76 88 78 41 94 84 85 30 45 38 27 25 31 33 37 90 90 90 90 91 91 91 90 89 89 89 88 82 82 82 88 70 68 68 68 70 67 61 61 61 67 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0
In [95]:
modelling_data_features = players_16.drop("value_eur", axis = 1)
modelling_data_target = players_16["value_eur"]
In [96]:
modelling_data_features.columns
Out[96]:
Index(['age', 'height_cm', 'weight_kg', 'overall', 'potential',
       'international_reputation', 'weak_foot', 'skill_moves', 'pace',
       'shooting',
       ...
       'SUB', 'High/High', 'High/Low', 'High/Medium', 'Low/High', 'Low/Low',
       'Low/Medium', 'Medium/High', 'Medium/Low', 'Medium/Medium'],
      dtype='object', length=114)

Normalization

In order to have better accuracy of the model, the values of all features should have same scale.

In [97]:
scaler = MinMaxScaler()
modelling_data_features_scaled=scaler.fit_transform(modelling_data_features)

Splitting the data

In [98]:
features_train, features_test, target_train, target_test = train_test_split(
    modelling_data_features_scaled, modelling_data_target, train_size = 0.7, test_size = 0.3, random_state = 42)
print(features_train.shape, features_test.shape, target_train.shape, target_test.shape, sep = "\r\n")
(10416, 114)
(4465, 114)
(10416,)
(4465,)

Run the Model

In [99]:
model = LinearRegression()
model.fit(features_train, target_train)
Out[99]:
LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)
In [100]:
score=model.score(features_test,target_test)
score
Out[100]:
0.6273786112802693

Check the model with dataset from a different year (2020)

In [101]:
players_20=pd.read_csv("data/players_20.csv",index_col="sofifa_id")
In [102]:
players_20.head()
Out[102]:
player_url short_name long_name age dob height_cm weight_kg nationality club overall potential value_eur wage_eur player_positions preferred_foot international_reputation weak_foot skill_moves work_rate body_type real_face release_clause_eur player_tags team_position team_jersey_number loaned_from joined contract_valid_until nation_position nation_jersey_number pace shooting passing dribbling defending physic gk_diving gk_handling gk_kicking gk_reflexes gk_speed gk_positioning player_traits attacking_crossing attacking_finishing attacking_heading_accuracy attacking_short_passing attacking_volleys skill_dribbling skill_curve skill_fk_accuracy skill_long_passing skill_ball_control movement_acceleration movement_sprint_speed movement_agility movement_reactions movement_balance power_shot_power power_jumping power_stamina power_strength power_long_shots mentality_aggression mentality_interceptions mentality_positioning mentality_vision mentality_penalties mentality_composure defending_marking defending_standing_tackle defending_sliding_tackle goalkeeping_diving goalkeeping_handling goalkeeping_kicking goalkeeping_positioning goalkeeping_reflexes ls st rs lw lf cf rf rw lam cam ram lm lcm cm rcm rm lwb ldm cdm rdm rwb lb lcb cb rcb rb
sofifa_id
158023 https://sofifa.com/player/158023/lionel-messi/... L. Messi Lionel Andrés Messi Cuccittini 32 1987-06-24 170 72 Argentina FC Barcelona 94 94 95500000 565000 RW, CF, ST Left 5 4 4 Medium/Low Messi Yes 195800000.0 #Dribbler, #Distance Shooter, #Crosser, #FK Sp... RW 10.0 NaN 2004-07-01 2021.0 NaN NaN 87.0 92.0 92.0 96.0 39.0 66.0 NaN NaN NaN NaN NaN NaN Beat Offside Trap, Argues with Officials, Earl... 88 95 70 92 88 97 93 94 92 96 91 84 93 95 95 86 68 75 68 94 48 40 94 94 75 96 33 37 26 6 11 15 14 8 89+2 89+2 89+2 93+2 93+2 93+2 93+2 93+2 93+2 93+2 93+2 92+2 87+2 87+2 87+2 92+2 68+2 66+2 66+2 66+2 68+2 63+2 52+2 52+2 52+2 63+2
20801 https://sofifa.com/player/20801/c-ronaldo-dos-... Cristiano Ronaldo Cristiano Ronaldo dos Santos Aveiro 34 1985-02-05 187 83 Portugal Juventus 93 93 58500000 405000 ST, LW Right 5 4 5 High/Low C. Ronaldo Yes 96500000.0 #Speedster, #Dribbler, #Distance Shooter, #Acr... LW 7.0 NaN 2018-07-10 2022.0 LS 7.0 90.0 93.0 82.0 89.0 35.0 78.0 NaN NaN NaN NaN NaN NaN Long Throw-in, Selfish, Argues with Officials,... 84 94 89 83 87 89 81 76 77 92 89 91 87 96 71 95 95 85 78 93 63 29 95 82 85 95 28 32 24 7 11 15 14 11 91+3 91+3 91+3 89+3 90+3 90+3 90+3 89+3 88+3 88+3 88+3 88+3 81+3 81+3 81+3 88+3 65+3 61+3 61+3 61+3 65+3 61+3 53+3 53+3 53+3 61+3
190871 https://sofifa.com/player/190871/neymar-da-sil... Neymar Jr Neymar da Silva Santos Junior 27 1992-02-05 175 68 Brazil Paris Saint-Germain 92 92 105500000 290000 LW, CAM Right 5 5 5 High/Medium Neymar Yes 195200000.0 #Speedster, #Dribbler, #Playmaker  , #Crosser,... CAM 10.0 NaN 2017-08-03 2022.0 LW 10.0 91.0 85.0 87.0 95.0 32.0 58.0 NaN NaN NaN NaN NaN NaN Power Free-Kick, Injury Free, Selfish, Early C... 87 87 62 87 87 96 88 87 81 95 94 89 96 92 84 80 61 81 49 84 51 36 87 90 90 94 27 26 29 9 9 15 15 11 84+3 84+3 84+3 90+3 89+3 89+3 89+3 90+3 90+3 90+3 90+3 89+3 82+3 82+3 82+3 89+3 66+3 61+3 61+3 61+3 66+3 61+3 46+3 46+3 46+3 61+3
200389 https://sofifa.com/player/200389/jan-oblak/20/... J. Oblak Jan Oblak 26 1993-01-07 188 87 Slovenia Atlético Madrid 91 93 77500000 125000 GK Right 3 3 1 Medium/Medium Normal Yes 164700000.0 NaN GK 13.0 NaN 2014-07-16 2023.0 GK 1.0 NaN NaN NaN NaN NaN NaN 87.0 92.0 78.0 89.0 52.0 90.0 Flair, Acrobatic Clearance 13 11 15 43 13 12 13 14 40 30 43 60 67 88 49 59 78 41 78 12 34 19 11 65 11 68 27 12 18 87 92 78 90 89 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
183277 https://sofifa.com/player/183277/eden-hazard/2... E. Hazard Eden Hazard 28 1991-01-07 175 74 Belgium Real Madrid 91 91 90000000 470000 LW, CF Right 4 4 4 High/Medium Normal Yes 184500000.0 #Speedster, #Dribbler, #Acrobat LW 7.0 NaN 2019-07-01 2024.0 LF 10.0 91.0 83.0 86.0 94.0 35.0 66.0 NaN NaN NaN NaN NaN NaN Beat Offside Trap, Selfish, Finesse Shot, Spee... 81 84 61 89 83 95 83 79 83 94 94 88 95 90 94 82 56 84 63 80 54 41 87 89 88 91 34 27 22 11 12 6 8 8 83+3 83+3 83+3 89+3 88+3 88+3 88+3 89+3 89+3 89+3 89+3 89+3 83+3 83+3 83+3 89+3 66+3 63+3 63+3 63+3 66+3 61+3 49+3 49+3 49+3 61+3

The new dataset has same format as the old one, so we will do the same steps for data cleaning.

In [103]:
players_20_for_modelling=players_20.drop(["player_url","long_name","wage_eur","release_clause_eur","mentality_composure", "nation_position", "nation_jersey_number", "loaned_from","player_tags"],axis=1)
In [104]:
players_20_for_modelling=players_20_for_modelling.drop(["gk_diving","gk_handling", "gk_kicking","gk_reflexes","gk_positioning"],axis=1)
In [105]:
cols_for_fill_20=['pace',
       'shooting', 'passing', 'dribbling', 'defending', 'physic', 'gk_speed',
       'attacking_crossing',
       'attacking_finishing', 'attacking_heading_accuracy',
       'attacking_short_passing', 'attacking_volleys', 'skill_dribbling',
       'skill_curve', 'skill_fk_accuracy', 'skill_long_passing',
       'skill_ball_control', 'movement_acceleration', 'movement_sprint_speed',
       'movement_agility', 'movement_reactions', 'movement_balance',
       'power_shot_power', 'power_jumping', 'power_stamina', 'power_strength',
       'power_long_shots', 'mentality_aggression', 'mentality_interceptions',
       'mentality_positioning', 'mentality_vision', 'mentality_penalties',
       'defending_marking', 'defending_standing_tackle',
       'defending_sliding_tackle', 'goalkeeping_diving',
       'goalkeeping_handling', 'goalkeeping_kicking',
       'goalkeeping_positioning', 'goalkeeping_reflexes', 'ls', 'st', 'rs',
       'lw', 'lf', 'cf', 'rf', 'rw', 'lam', 'cam', 'ram', 'lm', 'lcm', 'cm',
       'rcm', 'rm', 'lwb', 'ldm', 'cdm', 'rdm', 'rwb', 'lb', 'lcb', 'cb',
       'rcb', 'rb']
In [106]:
transform_columns_to_number(players_20_for_modelling,cols_for_fill_20)
In [107]:
players_20_for_modelling.isnull().sum().sort_values(ascending=False)
Out[107]:
player_traits                 10712
joined                         1288
contract_valid_until            240
team_jersey_number              240
team_position                   240
attacking_finishing               0
dribbling                         0
defending                         0
physic                            0
gk_speed                          0
attacking_crossing                0
attacking_heading_accuracy        0
shooting                          0
attacking_short_passing           0
attacking_volleys                 0
skill_dribbling                   0
skill_curve                       0
skill_fk_accuracy                 0
skill_long_passing                0
skill_ball_control                0
movement_acceleration             0
movement_sprint_speed             0
passing                           0
rb                                0
pace                              0
movement_reactions                0
age                               0
dob                               0
height_cm                         0
weight_kg                         0
nationality                       0
club                              0
overall                           0
potential                         0
value_eur                         0
player_positions                  0
preferred_foot                    0
international_reputation          0
weak_foot                         0
skill_moves                       0
work_rate                         0
body_type                         0
real_face                         0
movement_agility                  0
movement_balance                  0
rcb                               0
power_shot_power                  0
cf                                0
rf                                0
rw                                0
lam                               0
cam                               0
ram                               0
lm                                0
lcm                               0
cm                                0
rcm                               0
rm                                0
lwb                               0
ldm                               0
cdm                               0
rdm                               0
rwb                               0
lb                                0
lcb                               0
cb                                0
lf                                0
lw                                0
rs                                0
mentality_penalties               0
power_jumping                     0
power_stamina                     0
power_strength                    0
power_long_shots                  0
mentality_aggression              0
mentality_interceptions           0
mentality_positioning             0
mentality_vision                  0
defending_marking                 0
st                                0
defending_standing_tackle         0
defending_sliding_tackle          0
goalkeeping_diving                0
goalkeeping_handling              0
goalkeeping_kicking               0
goalkeeping_positioning           0
goalkeeping_reflexes              0
ls                                0
short_name                        0
dtype: int64
In [108]:
columns_20=["joined","team_position","contract_valid_until"]
fill_missing_data(players_20_for_modelling,columns)

Test the model and check the hypothesis with new data

In [109]:
players_20_for_modelling["team_jersey_number"]=players_20_for_modelling["team_jersey_number"].fillna(0)
In [110]:
players_20_for_modelling.isnull().sum().sort_values(ascending=False)
Out[110]:
player_traits                 10712
rb                                0
movement_reactions                0
shooting                          0
passing                           0
dribbling                         0
defending                         0
physic                            0
gk_speed                          0
attacking_crossing                0
attacking_finishing               0
attacking_heading_accuracy        0
attacking_short_passing           0
attacking_volleys                 0
skill_dribbling                   0
skill_curve                       0
skill_fk_accuracy                 0
skill_long_passing                0
skill_ball_control                0
movement_acceleration             0
movement_sprint_speed             0
pace                              0
contract_valid_until              0
joined                            0
value_eur                         0
age                               0
dob                               0
height_cm                         0
weight_kg                         0
nationality                       0
club                              0
overall                           0
potential                         0
player_positions                  0
team_jersey_number                0
preferred_foot                    0
international_reputation          0
weak_foot                         0
skill_moves                       0
work_rate                         0
body_type                         0
real_face                         0
team_position                     0
movement_agility                  0
movement_balance                  0
rcb                               0
power_shot_power                  0
cf                                0
rf                                0
rw                                0
lam                               0
cam                               0
ram                               0
lm                                0
lcm                               0
cm                                0
rcm                               0
rm                                0
lwb                               0
ldm                               0
cdm                               0
rdm                               0
rwb                               0
lb                                0
lcb                               0
cb                                0
lf                                0
lw                                0
rs                                0
mentality_penalties               0
power_jumping                     0
power_stamina                     0
power_strength                    0
power_long_shots                  0
mentality_aggression              0
mentality_interceptions           0
mentality_positioning             0
mentality_vision                  0
defending_marking                 0
st                                0
defending_standing_tackle         0
defending_sliding_tackle          0
goalkeeping_diving                0
goalkeeping_handling              0
goalkeeping_kicking               0
goalkeeping_positioning           0
goalkeeping_reflexes              0
ls                                0
short_name                        0
dtype: int64
In [111]:
players_20_for_modelling=players_20_for_modelling.drop(["short_name","dob","player_traits", "joined", "body_type", "real_face","contract_valid_until","player_positions","nationality","team_jersey_number","club"],axis=1)
In [112]:
players_20_for_modelling=players_20_for_modelling[['value_eur','age', 'height_cm', 'weight_kg', 'overall', 'potential',
       'preferred_foot', 'international_reputation', 'weak_foot',
       'skill_moves', 'work_rate', 'team_position', 'pace', 'shooting',
       'passing', 'dribbling', 'defending', 'physic', 'gk_speed',
       'attacking_crossing', 'attacking_finishing',
       'attacking_heading_accuracy', 'attacking_short_passing',
       'attacking_volleys', 'skill_dribbling', 'skill_curve',
       'skill_fk_accuracy', 'skill_long_passing', 'skill_ball_control',
       'movement_acceleration', 'movement_sprint_speed', 'movement_agility',
       'movement_reactions', 'movement_balance', 'power_shot_power',
       'power_jumping', 'power_stamina', 'power_strength', 'power_long_shots',
       'mentality_aggression', 'mentality_interceptions',
       'mentality_positioning', 'mentality_vision', 'mentality_penalties',
       'defending_marking', 'defending_standing_tackle',
       'defending_sliding_tackle', 'goalkeeping_diving',
       'goalkeeping_handling', 'goalkeeping_kicking',
       'goalkeeping_positioning', 'goalkeeping_reflexes', 'ls', 'st', 'rs',
       'lw', 'lf', 'cf', 'rf', 'rw', 'lam', 'cam', 'ram', 'lm', 'lcm', 'cm',
       'rcm', 'rm', 'lwb', 'ldm', 'cdm', 'rdm', 'rwb', 'lb', 'lcb', 'cb',
       'rcb', 'rb']]
In [113]:
dummy_features_foot_20=pd.get_dummies(players_20_for_modelling["preferred_foot"])
dummy_features_position_20=pd.get_dummies(players_20_for_modelling["team_position"])
dummy_features_rate_20=pd.get_dummies(players_20_for_modelling["work_rate"])
In [114]:
players_20_for_modelling=players_20_for_modelling.drop(["preferred_foot","team_position","work_rate"],axis=1)
In [115]:
players_20_for_modelling[['Left', 'Right']]=dummy_features_foot_20
players_20_for_modelling[['CAM', 'CB', 'CDM', 'CF', 'CM', 'GK', 'LAM', 'LB', 'LCB', 'LCM', 'LDM',
       'LF', 'LM', 'LS', 'LW', 'LWB', 'RAM', 'RB', 'RCB', 'RCM', 'RDM', 'RES',
       'RF', 'RM', 'RS', 'RW', 'RWB', 'ST', 'SUB']]=dummy_features_position_20
players_20_for_modelling[['High/High', 'High/Low', 'High/Medium', 'Low/High', 'Low/Low',
       'Low/Medium', 'Medium/High', 'Medium/Low', 'Medium/Medium']]=dummy_features_rate_20
In [116]:
modelling_data_20=pd.get_dummies(players_20_for_modelling)
In [117]:
modelling_data_features_20 = modelling_data_20.drop("value_eur", axis = 1)
modelling_data_target_20 = modelling_data_20["value_eur"]
In [118]:
scaler = MinMaxScaler()
modelling_data_features_scaled_20=scaler.fit_transform(modelling_data_features_20)
In [119]:
score=model.score(modelling_data_features_scaled_20,modelling_data_target_20 )
score
Out[119]:
0.5364270009269339

This is very low accuracy. Either data is not properly preprocessed, or the available data has not much relation to the price of players. Main features that influence value might not be present in the data.

Checking the Hypothesis

In [120]:
english_players_price_20=players_20[players_20["nationality"]=="England"]["value_eur"]
spanish_players_price_20=players_20[players_20["nationality"]=="Spain"]["value_eur"]
In [121]:
ttest_ind(english_players_price_20, spanish_players_price_20)
Out[121]:
Ttest_indResult(statistic=-12.978089447519524, pvalue=2.0915163608115403e-37)

Here the pvalue is even smaller than for 2016, which again leads to rejecting the H0 hypothesis. All this means that Spanish and English players do not have equal payment.

Future work

Many more analysis can be done with this dataset. Players_traits can be investigated. Do they have any influence on the value? Individual players progress through the years can be also checked (the complete set contains data for all years 2015 - 2020).

References

[1] Wikipedia
[2] https://www.guidetofootball.com/tactics/playing-positions/
[3] Yordan Darakchiev. SoftUni "Data Science" course, Working-with-Spatial-Data-and-Network-Analysis, 2020.
[4] Yordan Darakchiev. SoftUni "Data Science" course, Regression Models, 2020.
[5] Jake VanderPlas. Python Data Science Handbook. O'Reilly, 2017.